攻壳机动队——人类的进化与异化

《攻壳机动队》以尖利的视角来审视人类的进化与异化,人类个体的机械化,是与通过创作人型机器人实现造人梦想全然相反的过程,然而两者的目标却是相同的;超越自然的束缚,规避死亡的宿命,实现人类的"下一次进化"。但机械化与信息化这两者本是一体两面的存在,成为"神"并不会化解我们身为"人"时所处的生存困境,科技也许可以加速人类社会的进程,但它不仅无法告知人类应该如何去处置自身的存在,还会催生出更为严重的身份认同危机。

一体哲学与新人类文化（英文）

宇宙中不断变化的银河原则对当今人类和地球产生了极大的影响。地球上的问题很多,如环境恶化、食品短缺、宗教与宗教之间的冲突和敌对、自私自利的个人主义以及人类所面临的各种危险处境等。然而,30年前,在东方经文中出现了“克己胜者”（新人类或义人的存在方式）,这种人超越了自我（即死亡的精灵）并作为圣灵获得了重生。通过发动一场新的文化运动,这种人可以把旧世界（它使众生死于无知和缺乏沟通）变成一个不朽的新世界。所有的经文和未来主义者都预测说,新宗教运动、一体科学和一体哲学的终极目的是要建立这样一个天堂,并实现对人类的救赎。人类将要重获的最大善,就是可以像神一样得到永生,韩国已经开始了这种追求。韩国新宗教运动将开启一个义人的时代,它需要新的伦理、公共秩序、公共哲学以及新的规则来建立这样一个新世界。本文介绍了作为一种新哲学及新科学的“一体哲学”的精华,阐述了新人类（也就是神人）对生命的拯救。这种文化很快会成为世界主流文化。最重要的是,公共哲学是大一体光明思想,也是一体哲学,它把所有人视为一个家庭,并给人类带来和平。通过不断沟通与融合,在一体哲学思想指导下推进生命救赎运动,保守新天堂的法则,世界将迎来一个新时代,我认为,文本所讨论的永生哲学和新（神）人类新文化,将会成为世界主流文化,使所有的人演变为一体,并给人带来真正的幸福。

Gated Neural Network-Based Unsteady Aerodynamic Modeling for Large Angles of Attack

Modeling of unsteady aerodynamic loads at high angles of attack using a small amount of experimental or simulation data to construct predictive models for unknown states can greatly improve the efficiency of aircraft unsteady aerodynamic design and flight dynamics analysis.In this paper,aiming at the problems of poor generalization of traditional aerodynamic models and intelligent models,an intelligent aerodynamic modeling method based on gated neural units is proposed.The time memory characteristics of the gated neural unit is fully utilized,thus the nonlinear flow field characterization ability of the learning and training process is enhanced,and the generalization ability of the whole prediction model is improved.The prediction and verification of the model are carried out under the maneuvering flight condition of NACA0015 airfoil.The results show that the model has good adaptability.In the interpolation prediction,the maximum prediction error of the lift and drag coefficients and the moment coefficient does not exceed 10%,which can basically represent the variation characteristics of the entire flow field.In the construction of extrapolation models,the training model based on the strong nonlinear data has good accuracy for weak nonlinear prediction.Furthermore,the error is larger,even exceeding 20%,which indicates that the extrapolation and generalization capabilities need to be further optimized by integrating physical models.Compared with the conventional state space equation model,the proposed method can improve the extrapolation accuracy and efficiency by 78%and 60%,respectively,which demonstrates the applied potential of this method in aerodynamic modeling.

Oxygen Reduction Reaction Activity of Fe-based Dual-Atom Catalysts with Different Local Configurations via Graph Neural Representation

The performance of proton exchange membrane fuel cells depends heavily on the oxygen reduction reaction(ORR)at the cathode,for which platinum-based catalysts are currently the standard.The high cost and limited availability of platinum have driven the search for alternative catalysts.While FeN4 single-atom catalysts have shown promising potential,their ORR activity needs to be further enhanced.In contrast,dual-atom catalysts(DACs)offer not only higher metal loading but also the ability to break the ORR scaling relations.However,the diverse local structures and tunable coordination environments of DACs create a vast chemical space,making large-scale computational screening challenging.In this study,we developed a graph neural network(GNN)-based framework to predict the ORR activity of Fe-based DACs,effectively addressing the challenges posed by variations in local catalyst structures.Our model,trained on a dataset of 180 catalysts,accurately predicted the Gibbs free energy of ORR intermediates and overpotentials,and identified 32 DACs with superior catalytic activity compared to FeN4 SAC.This approach not only advances the design of high-performance DACs,but also offers a powerful computational tool that can significantly reduce the time and cost of catalyst development,thereby accelerating the commercialization of fuel cell technologies.

Damage effect and mechanisms of cyclophosphamide to human neuroblastoma SH-SY5Y cells

OBJECTIVE To investigate the damage effect and mechanisms of cyclophosphamide(CTX)and its active metabolite derivative 4-hydroperoxycyclophosphamide(4-HC)to human neuroblas⁃toma SH-SY5Y cells.METHODS SH-SY5Y cells were treated with CTX[0(cell control),0.01,0.1,1,5,10,20,40 and 80 mmol·L^(-1)]and 4-HC[0(cell control),0.01,0.1,1,5,10,20,40 and 80μmol·L^(-1)]for 48 h.Cell confluence and morphology were observed by the IncuCyte ZOOM system.Cell viability was assessed by CCK-8 assay.Lactate dehydrogenase(LDH)release was measured by LDH assay kit.SH-SY5Y cells were treated with CTX(0,1,5,10 and 20 mmol·L^(-1))and 4-HC(0,1,5,10 and 20μmol·L^(-1))for 48 h before cell proliferation was analyzed by 5-ethynyl-2′-deoxyuridine(EdU)staining assay.Immunofluorescence was employed to assess the levels of the DNA double-strand break markerγ-H2AX and to evaluate changes in mitochondrial membrane potential.SH-SY5Y cells were treated with CTX(0,1,5 and 10 mmol·L^(-1))and 4-HC(0,1,5 and 10μmol·L^(-1))for 48 h,and the alterations in glycolysis and oxidative phosphorylation levels were analyzed using the Seahorse XFe96 Analyzer.RESULTS Compared with the cell control group,cell confluence and cell viability were significantly reduced in the CTX and 4-HC groups(P<0.01),and the half-maximal inhibitory concentrations(IC50)for CTX and 4-HC were 4.44 mmol·L^(-1) and 4.78μmol·L^(-1),respectively.The release rate of LDH was signif⁃icantly increased while the percentage of EdU+cells was significantly reduced in the CTX and 4-HC groups(P<0.01).The percentage ofγ-H2AX+cells was significantly increased and mitochondrial membrane potential significantly decreased in the CTX and 4-HC group(P<0.05).Treatment with CTX and 4-HC resulted in reduced levels of maximum glycolytic capacity,glycolytic reserve,maximal respi⁃ration,and ATP production(P<0.05).CONCLUSION CTX and 4-HC exert significant cytotoxic effects on SH-SY5Y cells by disrupting cell membrane structure,impeding cell proliferation,and reducing cell viability.The mechanisms underlying these effects may involve intracellular DNA damage,disturbance of energy metabolism and mitochondrial dysfunction.

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

CHAOTIC ANNEALING NEURAL NETWORK FOR GLOBAL OPTIMIZATION OF CONSTRAINED NONLINEAR PROGRAMMING

Chaotic neural networks have global searching ability.But their applications are generally confined to combinatorial optimization to date.By introducing chaotic noise annealing process into conventional Hopfield network,this paper proposes a new chaotic annealing neural network (CANN) for global optimization of continuous constrained non linear programming.It is easy to implement,conceptually simple,and generally applicable.Numerical experiments on severe test functions manifest that CANN is efficient and reliable to search for global optimum and outperforms the existing genetic algorithm GAMAS for the same purpose.

Pathogenesis of Parkinson＇s disease： oxidative stress, environmental impact factors and inflammatory processes

Current hypothesis of neuronal degeneration in Parkinson＇s disease （PD） have been proposed, including formation of free radicals and oxidative stress, mitochondrial dysfunction, excitotoxicity, trophic factor deficiency, inflammatory processes, genetic factors, environmental impact factors, toxic action of nitric oxide, apoptosis, and so on. This review mainly discussed oxidative stress, environmental impact factors, and inflammatory processes in PD.

Neuropeptide Y promotes TGF-β1 production in RAW264.7 cells by activating PI3K pathway via Y1 receptor

Objective To examine the effect of neuropeptide Y （NPY） on TGF-β1 production in RAW264.7 macrophages. Methods Enzyme linked immunosorbent assay （ELISA） was used to detect TGF-β1 production. Cell counting kit 8 （CCK-8） was used to assay the viability of RAW264.7 cells. Western blot was used to detect the phosphorylation of PI3K p85. Results NPY treatment could promote TGF-β1 production and rapid phosphorylation of PI3K p85 in RAW264.7 cells via Y1 receptor. The elevated TGF-β 1 production induced by NPY could be abolished by wortrnannin pretreatment. Conclusion NPY may elicit TGF-β production in RAW264.7 cells via Y1 receptor, and the activated PI3K pathway may account for this effect.

In vitro Study of Interstitial Combination Chemotherapy in the Treatment of Glioma

Objective： To investigate the inhibitory effects of combination chemotherapy of Carboplatin （CBP）, Teniposide （Vm-26）, Methasquin （MTX）, and Nimodipine （NIM） on glioma, and to explore the sensitivity of glioma cells to different treatment regimens so as to provide some clues for clinical usage of interstitial combination chemotherapy. Methods： MTT assay and 3H-TdR incorporation assay were performed to evaluate the inhibitory effects upon the proliferation of glioma cells, and to compare the sen- sitivity of glioma cells to administration of CBP, Vm-26, MTX, and NIM with that of the administration of CBP＋NIM, Vm-26＋NIM, MTX＋NIM, CBP＋Vm-26＋MTX, or CBP＋Vm-26＋MTX＋NIM, respectively. Results： The inhibition rate of CBP＋Vm-26＋MTX＋NIM combination administration against glioma cells was 96.64%, higher than that of CBP＋NIM （69.03%）, Vm-26＋NIM （71.53%）, MTX＋NIM （52.75%）, CBP＋Vm-26＋MTX （78.59%） （P〈0.01）, and the dosage of CBP, Vm-26, and MTX was declined to 1/10- 1/100 that of respective use of CBP, Vm-26, and MTX. Conclusion： The curative effect of combination administration of CBP, Vm-26, MTX, and NIM was much better than that of respective administration, suggesting a higher inhibition rate and a lower dosage use.

DAMAGE CLASSIFICATION BY PROBABILISTIC NEURAL NETWORKS BASED ON LATENT COMPONENTS FOR TIME-VARYING SYSTEM

A new approach to damage classification for health monitoring of a time-varylng system is presented. The functional-series time-dependent auto regressive moving average （FS-TARMA） time series model is applied to the vibration signal observed in the time-varying system for estimating the TAR/TMA parameters and the innovation variance. These parameters are the functions of the time, represented by a group of projection coefficients on the certain functional subspace with specific basis functions. The estimated TAR/TMA parameters and the innovation variance are further used to calculate the latent components （LCs） as the more informative data for health monitoring evaluation, based on an eigenvalue decomposition technique. LCs are then combined and reduced to numerical values （NVs） as feature sets, which are input to a probabilistic neural network （PNN） for the damage classification. For the evaluation of the proposed method, numerical simulations of the damage classification for a tlme-varylng system are used, in which different classes of damage are modeled by the mass or stiffness reductions. It is demonstrated that the method can identify the damages in the course of operation and the change of parameters on the time-varying background of the system.

A New Hybrid Method for Constrained Global Optimization

By combining properly the simulated annealing algorithm and the nonlinear programming neural network, a new hybrid method for comtrained global optimization is proposed in this paper. To maintain the applicability of the simulated annealing algorithm used in the hybrid method as general as possible, the nonlinear programming neural network is employed at each iteration to find only a feasible solution to the original constrained problem rather than a local optimal solution. Such a feasible solution is obtained by solving an auxiliary optimization problem with a new objective function. The computational results for two numerical examples indicate that the proposed hybrid method for constrained global optimization is not only highly reliable but also much more effcient than the simulated annealing algorithm using the penalty function method to deal with the constraints.

Effect of Weak Noise on the Frequency Tuning of Mouse Inferior Collicular Neurons

In order to study the effect of weak noise on the sound signal extraction of mouse (Mus musculus Km) inferior collicular (IC) neurons from environments,we examined the changes in frequency tuning curves (FTCs) of 32 neurons induced by a weak noise relative to 5 dB below minimum threshold of tone (reMT-5 dB) under free field stimulation conditions.The results were as follows:① There were three types of variations in FTCs,sharpened (34.4%),broadened (18.8%),and unaffected (46.9%),nevertheless,only the alteration of sharpened FTCs was statistically different.② Sharpness of frequency tuning induced by a reMT-5 dB noise was very strong.Q 10 and Q 30 of FTCs were increased by (34.42±17.04)% (P=0.026,n=11) and (46.34±22.88)% (P=0.009,n=7).③ The changes of inverse-slopes (ISs,kHz/dB) between high (IS high) and low (IS low) limbs of FTCs were dissymmetry.The IS high of FTCs decreased markedly (P=0.046,n=7),however,there was little change (P=0.947,n=7) in IS low.Our data revealed for the first time that the weak noise could sharpen frequency tuning and increase the sensitivity on the high frequency of sound signal in IC neurons of mouse.

Regulatory genes controlling neural stem cells differentiation into neurons

The recent progress in neural stem cells （NSCs） research has shed lights on possibility of repair and restoration of neuronal function in neurodegenerative diseases using stem cells. Induction of stem cells differentiate into mature neurons is critical to achieve the clinical applications of NSCs. At present, molecular mechanisms modulating NSC differentiation are not fully understood. Differentiation of stem cells into neuronal and glial cells involves an array of changes in expression of transcription factors. Transcription factors then trigger the expression of a variety of central nervous system （CNS） genes that lead NSCs to differentiate towards different cell types. In this paper, we summarized the recent findings on the gene regulation of NSCs differentiation into neuronal cells.

Combining the genetic algorithms with artificial neural networks for optimization of board allocating

This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.

NEURAL NETWORK REALIZATION OF STRUCTURAL VIBRATION CONTROL USING MR DAMPER

Magnetorheological (MR) dampers are one of the most promising new devices for civil infrastructural vibration control applications. However, due to their highly nonlinear dynamic behavior, it is very difficult to obtain of a mathematical model of inverse MR damper that has an explicit relationship between the desired damper force and the command signal (voltage). This force voltage relationship is especially required for the structural vibration control design and simulation using MR dampers. This paper focuses on using a neural network (NN) technique to emulate the inverse MR damper model. The output of the neural network can be used to command the MR damper for generating desired forces. Numerical simulations are also presented to illustrate the effectiveness of this inverse model in semi active vibration control using MR dampers.

TWO-DIMENSIONAL STOCHASTIC AIRFOIL OPTIMIZATION DESIGN METHOD BASED ON NEURAL NETWORKS

To avoid the aerodynamic performance loss of airfoil at non-design state which often appears in single point design optimization, and to improve the adaptability to the uncertain factors in actual flight environment, a two-dimensional stochastic airfoil optimization design method based on neural networks is presented. To provide highly efficient and credible analysis, four BP neural networks are built as surrogate models to predict the airfoil aerodynamic coefficients and geometry parameter. These networks are combined with the probability density function obeying normal distribution and the genetic algorithm, thus forming an optimization design method. Using the method, for GA（W）-2 airfoil, a stochastic optimization is implemented in a two-dimensional flight area about Mach number and angle of attack. Compared with original airfoil and single point optimization design airfoil, results show that the two-dimensional stochastic method can improve the performance in a specific flight area, and increase the airfoil adaptability to the stochastic changes of multiple flight parameters.

Optimization of Plasma Etching Parameters and Mask for Silica Optical Waveguides

Optical waveguides in silica-on-silicon are one of the key elements in optical communications.The processes of deep etching silica waveguides using resist and metal masks in RIE plasma are investigated.The etching responses,including etching rate and selectivity as functions of variation of parameters,are modeled with a 3D neural network.A novel resist/metal combined mask that can overcome the single-layer masks’ limitations is developed for enhancing the waveguides deep etching and low-loss optical waveguides are fabricated at last.

Pathogenesis of Parkinson’s disease: oxidative stress, environmental impact factors and inflammatory processes

Current hypothesis of neuronal degeneration in Parkinson’s disease (PD) have been proposed, including formation of free radicals and oxidative stress, mitochondrial dysfunction, excitotoxicity, trophic factor deficiency, inflam- matory processes, genetic factors, environmental impact factors, toxic action of nitric oxide, apoptosis, and so on. This review mainly discussed oxidative stress, environmental impact factors, and inflammatory processes in PD.

Communities and Quantitative Analysis of Ammonia-oxidizing Organisms in Pearl River Estuary Sediments

[Objective] This study aimed to investigate the abundance and composition of ammonia-oxidizing bacteria and ammonia-oxidizing archaea in Pearl River Estuary sediment.[Method] Firstly,the amoA gene library was constructed;then based on that,the content and diversity of amoA genes of ammonia-oxidizing bacteria and ammonia-oxidizing archaea in Pearl River Estuary sediment were detected by using quantitative real-time polymerase chain reaction（Q-PCR）.[Result] The results of Q-PCR presented that ammonia-oxidizing archaea（AOA） were more abundant than ammonia-oxidizing bacteria（AOB） in the top of sediment cores,with ratios of AOA to AOB of 22 and 9 at the two sites.It suggested that ammonia-oxidizing archaea may play more important roles than ammonia-oxidizing bacteria in the process of ammonia oxidation in the Pearl River Estuary sediment.The phylogenetic tree based on amoA gene sequences revealed that the amoA sequences of both AOA and AOB shared high similarity with the clones from uncultured environment.In the top sediment layer at site Q7,AOB amoA-like gene sequences were dominated by Nitrosomonas-like sequence types,which could be classified into five groups（clusters A,B,C,D and E）.Cluster A accounted for 72.1% of the library.In the top sediment layer,the AOA amoA gene fell into two groups ＂water column/sediment＂ cluster（52.2%） and ＂soil/sediment＂ cluster（47.8%）.But in the bottom sediment layer of Q7,most of the AOA amoA sequences（93.3%） fell into ＂soil/sediment＂ cluster,and a little part（6.7%） fell into the ＂water/sediment＂ cluster.In addition,the total amount of amoA genes in the bottom sediment was higher than that in top sediment.[Conclusion] This study helps to realize the cycle of nitrogen in Pearl River Estuary Region,and thus to provide theoretical support for the treatment of nitrogen eutrophication.