A Universal Activation Function for Deep Learning

Recently,deep learning has achieved remarkable results in fields that require human cognitive ability,learning ability,and reasoning ability.Activation functions are very important because they provide the ability of artificial neural networks to learn complex patterns through nonlinearity.Various activation functions are being studied to solve problems such as vanishing gradients and dying nodes that may occur in the deep learning process.However,it takes a lot of time and effort for researchers to use the existing activation function in their research.Therefore,in this paper,we propose a universal activation function(UA)so that researchers can easily create and apply various activation functions and improve the performance of neural networks.UA can generate new types of activation functions as well as functions like traditional activation functions by properly adjusting three hyperparameters.The famous Convolutional Neural Network(CNN)and benchmark datasetwere used to evaluate the experimental performance of the UA proposed in this study.We compared the performance of the artificial neural network to which the traditional activation function is applied and the artificial neural network to which theUA is applied.In addition,we evaluated the performance of the new activation function generated by adjusting the hyperparameters of theUA.The experimental performance evaluation results showed that the classification performance of CNNs improved by up to 5%through the UA,although most of them showed similar performance to the traditional activation function.

Activation Functions Effect on Fractal Coding Using Neural Networks

Activation functions play an essential role in converting the output of the artificial neural network into nonlinear results,since without this nonlinearity,the results of the network will be less accurate.Nonlinearity is the mission of all nonlinear functions,except for polynomials.The activation function must be dif-ferentiable for backpropagation learning.This study’s objective is to determine the best activation functions for the approximation of each fractal image.Different results have been attained using Matlab and Visual Basic programs,which indi-cate that the bounded function is more helpful than other functions.The non-lin-earity of the activation function is important when using neural networks for coding fractal images because the coefficients of the Iterated Function System are different according to the different types of fractals.The most commonly cho-sen activation function is the sigmoidal function,which produces a positive value.Other functions,such as tansh or arctan,whose values can be positive or negative depending on the network input,tend to train neural networks faster.The coding speed of the fractal image is different depending on the appropriate activation function chosen for each fractal shape.In this paper,we have provided the appro-priate activation functions for each type of system of iterated functions that help the network to identify the transactions of the system.

Elevated brain temperature under severe heat exposure impairs cortical motor activity and executive function

Background:Excessive heat exposure can lead to hyperthermia in humans,which impairs physical performance and disrupts cognitive function.While heat is a known physiological stressor,it is unclear how severe heat stress affects brain physiology and function.Methods:Eleven healthy participants were subjected to heat stress from prolonged exercise or warm water immersion until their rectal temperatures(T_(re))attained 39.5℃,inducing exertional or passive hyperthermia,respectively.In a separate trial,blended ice was ingested before and during exercise as a cooling strategy.Data were compared to a control condition with seated rest(normothermic).Brain temperature(T_(br)),cerebral perfusion,and task-based brain activity were assessed using magnetic resonance imaging techniques.Results:T_(br)in motor cortex was found to be tightly regulated at rest(37.3℃±0.4℃(mean±SD))despite fluctuations in T_(re).With the development of hyperthermia,T_(br)increases and dovetails with the rising T_(re).Bilateral motor cortical activity was suppressed during high-intensity plantarflexion tasks,implying a reduced central motor drive in hyperthermic participants(T_(re)=38.5℃±0.1℃).Global gray matter perfusion and regional perfusion in sensorimotor cortex were reduced with passive hyperthermia.Executive function was poorer under a passive hyperthermic state,and this could relate to compromised visual processing as indicated by the reduced activation of left lateral-occipital cortex.Conversely,ingestion of blended ice before and during exercise alleviated the rise in both T_(re)and T_(bc)and mitigated heat-related neural perturbations.Conclusion:Severe heat exposure elevates T_(br),disrupts motor cortical activity and executive function,and this can lead to impairment of physical and cognitive performance.

Multistability of delayed complex-valued recurrent neural networks with discontinuous real-imaginarytype activation functions

In this paper, the multistability issue is discussed for delayed complex-valued recurrent neural networks with discontinuous real-imaginary-type activation functions. Based on a fixed theorem and stability definition, sufficient criteria are established for the existence and stability of multiple equilibria of complex-valued recurrent neural networks. The number of stable equilibria is larger than that of real-valued recurrent neural networks, which can be used to achieve high-capacity associative memories. One numerical example is provided to show the effectiveness and superiority of the presented results.

Neural Networks on an FPGA and Hardware-Friendly Activation Functions

This paper describes our implementation of several neural networks built on a field programmable gate array (FPGA) and used to recognize a handwritten digit dataset—the Modified National Institute of Standards and Technology (MNIST) database. We also propose a novel hardware-friendly activation function called the dynamic Rectifid Linear Unit (ReLU)—D-ReLU function that achieves higher performance than traditional activation functions at no cost to accuracy. We built a 2-layer online training multilayer perceptron (MLP) neural network on an FPGA with varying data width. Reducing the data width from 8 to 4 bits only reduces prediction accuracy by 11%, but the FPGA area decreases by 41%. Compared to networks that use the sigmoid functions, our proposed D-ReLU function uses 24% - 41% less area with no loss to prediction accuracy. Further reducing the data width of the 3-layer networks from 8 to 4 bits, the prediction accuracies only decrease by 3% - 5%, with area being reduced by 9% - 28%. Moreover, FPGA solutions have 29 times faster execution time, even despite running at a 60× lower clock rate. Thus, FPGA implementations of neural networks offer a high-performance, low power alternative to traditional software methods, and our novel D-ReLU activation function offers additional improvements to performance and power saving.

Theoretical Study on the C-H Activation in Decarbonylation of Acetaldehyde by NiL_2(L=SO_3CH_3) Using Density Functional Theory

Density functional theory calculations were carried out to explore the potential energy surface（PES） associated with the gas-phase reaction of Ni L2（L=SO3CH3） with acetone. The geometries and energies of the reactants, intermediates, products and transition states of the triplet ground potential energy surfaces of [Ni, O, C2, H4] were obtained at the B3LYP/6-311＋＋G（d,p） levels in C,H,O atoms and B3LYP/ Lanl2 dz in Ni atom. It was found through our calculations that the decabonylation of acetaldehyde contains four steps including encounter complexation, C-C activation, aldehyde H-shift and nonreactive dissociation. The results revealed that C-C activation induced by Ni L2（L=SO3CH3） led to the decarbonylation of acetaldehyde.

Convolution-Based Heterogeneous Activation Facility for Effective Machine Learning of ECG Signals

Machine Learning(ML)and Deep Learning(DL)technologies are revolutionizing the medical domain,especially with Electrocardiogram(ECG),by providing new tools and techniques for diagnosing,treating,and preventing diseases.However,DL architectures are computationally more demanding.In recent years,researchers have focused on combining the computationally less intensive portion of the DL architectures with ML approaches,say for example,combining the convolutional layer blocks of Convolution Neural Networks(CNNs)into ML algorithms such as Extreme Gradient Boosting(XGBoost)and K-Nearest Neighbor(KNN)resulting in CNN-XGBoost and CNN-KNN,respectively.However,these approaches are homogenous in the sense that they use a fixed Activation Function(AFs)in the sequence of convolution and pooling layers,thereby limiting the ability to capture unique features.Since various AFs are readily available and each could capture unique features,we propose a Convolutionbased Heterogeneous Activation Facility(CHAF)which uses multiple AFs in the convolution layer blocks,one for each block,with a motivation of extracting features in a better manner to improve the accuracy.The proposed CHAF approach is validated on PTB and shown to outperform the homogeneous approaches such as CNN-KNN and CNN-XGBoost.For PTB dataset,proposed CHAF-KNN has an accuracy of 99.55%and an F1 score of 99.68%in just 0.008 s,outperforming the state-of-the-art CNN-XGBoost which has an accuracy of 99.38%and an F1 score of 99.32%in 1.23 s.To validate the generality of the proposed CHAF,experiments were repeated on MIT-BIH dataset,and the proposed CHAF-KNN is shown to outperform CNN-KNN and CNN-XGBoost.

Interpretation and characterization of rate of penetration intelligent prediction model

Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations and machine learning algorithms,its lack of interpretability undermines its credibility.This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation function.By leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector operations.The FNN model is linearly characterized through further simplification,enabling its interpretation and analysis.The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield.The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well.The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity.In the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a corresponding linear representation equation.Furthermore,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section.The established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.

Uncertainty-Aware Physical Simulation of Neural Radiance Fields for Fluids

This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from2Dimages.This approach reconstructs color and density fields from 2D images using Neural Radiance Field(NeRF)and improves image quality using frequency regularization.The NeRF model is obtained via joint training ofmultiple artificial neural networks,whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel.In addition,customized physics-informed neural network(PINN)with residual blocks and two-layer activation functions are utilized to input the density fields of the NeRF into Navier-Stokes equations and convection-diffusion equations to reconstruct the velocity field.The velocity uncertainties are also evaluated through ensemble learning.The effectiveness of the proposed algorithm is demonstrated through numerical examples.The presentmethod is an important step towards downstream tasks such as reliability analysis and robust optimization in engineering design.

H_(∞)/Passive Synchronization of Semi-Markov Jump Neural Networks Subject to Hybrid Attacks via an Activation Function Division Approach

In this work,an H_(∞)/passive-based secure synchronization control problem is investigated for continuous-time semi-Markov neural networks subject to hybrid attacks,in which hybrid attacks are the combinations of denial-of-service attacks and deception attacks,and they are described by two groups of independent Bernoulli distributions.On this foundation,via the Lyapunov stability theory and linear matrix inequality technology,the H_(∞)/passive-based performance criteria for semi-Markov jump neural networks are obtained.Additionally,an activation function division approach for neural networks is adopted to further reduce the conservatism of the criteria.Finally,a simulation example is provided to verify the validity and feasibility of the proposed method.

Large-scale self-normalizing neural networks

Self-normalizing neural networks(SNN)regulate the activation and gradient flows through activation functions with the self-normalization property.As SNNs do not rely on norms computed from minibatches,they are more friendly to data parallelism,kernel fusion,and emerging architectures such as ReRAM-based accelerators.However,existing SNNs have mainly demonstrated their effectiveness on toy datasets and fall short in accuracy when dealing with large-scale tasks like ImageNet.They lack the strong normalization,regularization,and expression power required for wider,deeper models and larger-scale tasks.To enhance the normalization strength,this paper introduces a comprehensive and practical definition of the self-normalization property in terms of the stability and attractiveness of the statistical fixed points.It is comprehensive as it jointly considers all the fixed points used by existing studies:the first and second moment of forward activation and the expected Frobenius norm of backward gradient.The practicality comes from the analytical equations provided by our paper to assess the stability and attractiveness of each fixed point,which are derived from theoretical analysis of the forward and backward signals.The proposed definition is applied to a meta activation function inspired by prior research,leading to a stronger self-normalizing activation function named‘‘bi-scaled exponential linear unit with backward standardized’’(bSELU-BSTD).We provide both theoretical and empirical evidence to show that it is superior to existing studies.To enhance the regularization and expression power,we further propose scaled-Mixup and channel-wise scale&shift.With these three techniques,our approach achieves 75.23%top-1 accuracy on the ImageNet with Conv MobileNet V1,surpassing the performance of existing self-normalizing activation functions.To the best of our knowledge,this is the first SNN that achieves comparable accuracy to batch normalization on ImageNet.

Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.

Progress on the melanoidins produced by the Maillard reaction of fermented food and traditional Chinese medicine

FMs(Food-borne melanoidins)are brown high molecular weight polymers formed by the Mailiard reaction between carbohydrates and nitrogen-containing compounds during the processing of food or Traditional Chinese Medicine(TCM),and are widely found in food-borne products such as TCM concoctions,bakery,brewing,soy sauce,ferment and other food-borne products.FMs not only have a variety of biological activities,such as antioxidant,antibacterial,immunomodulation,regulation of intestinal flora,etc.,and can change the color,aroma and taste of food.The diversity of its components has become a research hotspot at home and abroad in recent years,with a wide range of application prospects.Therefore,this paper summarizes the existing information on FMs at home and abroad,mainly describes their preparation process,physicochemical properties,structural characteristics and functional activity research progress.Typical FMs,such as coffee,biscuits,wine and soy sauce in daily food,and Polygonatum,Perilla oil,Black ginseng,and Red jujube in T,were highlighted.Summarising the current status of research between the chemistry and pharmacodynamics of relevant FMs and presenting challenges and future recommendations for melanoidin research.In future research on FMs,one should pay more attention to basic research,especially isolation and purification and generation mechanisms,to further demonstrate the biological activity of FMs in vivo and in clinical trials.Thus,the potential value of its existence is deeply exploited to meet the needs of technology,production and health.

Microbial community structure and functional metabolic diversity are associated with organic carbon availability in an agricultural soil

Exploration of soil environmental characteristics governing soil microbial community structure and activity may improve our understanding of biogeochemical processes and soil quality. The impact of soil environmental characteristics especially organic carbon availability after 15-yr different organic and inorganic fertilizer inputs on soil bacterial community structure and functional metabolic diversity of soil microbial communities were evaluated in a 15-yr fertilizer experiment in Changping County, Beijing, China. The experiment was a wheat-maize rotation system which was established in 1991 including four different fertilizer treatments. These treatments included： a non-amended control（CK）, a commonly used application rate of inorganic fertilizer treatment（NPK）; a commonly used application rate of inorganic fertilizer with swine manure incorporated treatment（NPKM）, and a commonly used application rate of inorganic fertilizer with maize straw incorporated treatment（NPKS）. Denaturing gradient gel electrophoresis（DGGE） of the 16 S r RNA gene was used to determine the bacterial community structure and single carbon source utilization profiles were determined to characterize the microbial community functional metabolic diversity of different fertilizer treatments using Biolog Eco plates. The results indicated that long-term fertilized treatments significantly increased soil bacterial community structure compared to CK. The use of inorganic fertilizer with organic amendments incorporated for long term（NPKM, NPKS） significantly promoted soil bacterial structure than the application of inorganic fertilizer only（NPK）, and NPKM treatment was the most important driver for increases in the soil microbial community richness（S） and structural diversity（H）. Overall utilization of carbon sources by soil microbial communities（average well color development, AWCD） and microbial substrate utilization diversity and evenness indices（H＇ and E） indicated that long-term inorganic fertilizer with organic amendments incorporated（NPKM, NPKS） could significantly stimulate soil microbial metabolic activity and functional diversity relative to CK, while no differences of them were found between NPKS and NPK treatments. Principal component analysis（PCA） based on carbon source utilization profiles also showed significant separation of soil microbial community under long-term fertilization regimes and NPKM treatment was significantly separated from the other three treatments primarily according to the higher microbial utilization of carbohydrates, carboxylic acids, polymers, phenolic compounds, and amino acid, while higher utilization of amines/amides differed soil microbial community in NPKS treatment from those in the other three treatments. Redundancy analysis（RDA） indicated that soil organic carbon（SOC） availability, especially soil microbial biomass carbon（Cmic） and Cmic/SOC ratio are the key factors of soil environmental characteristics contributing to the increase of both soil microbial community structure and functional metabolic diversity in the long-term fertilization trial. Our results showed that long-term inorganic fertilizer and swine manure application could significantly improve soil bacterial community structure and soil microbial metabolic activity through the increases in SOC availability, which could provide insights into the sustainable management of China＇s soil resource.

Altered intrinsic functional connectivity of the primary visual cortex in youth patients with comitant exotropia: a resting state fMRI study

AIM： To evaluate the differences in the functional connectivity（FC） of the primary visual cortex（V1） between the youth comitant exotropia（CE） patients and health subjects using resting functional magnetic resonance imaging（f MRI） data.METHODS： Totally, 32 CEs（25 males and 7 females） and 32 healthy control subjects（HCs）（25 males and 7 females） were enrolled in the study and underwent the MRI scanning. Two-sample t-test was used to examine differences in FC maps between the CE patients and HCs. RESULTS： The CE patients showed significantly less FC between the left brodmann area（BA17） and left lingual gyrus/cerebellum posterior lobe, right middle occipital gyrus, left precentral gyrus/postcentral gyrus and right inferior parietal lobule/postcentral gyrus. Meanwhile, CE patients showed significantly less FC between right BA17 and right middle occipital gyrus（BA19, 37）.CONCLUSION： Our findings show that CE involves abnormal FC in primary visual cortex in many regions, which may underlie the pathologic mechanism of impaired fusion and stereoscopic vision in CEs.

The cortical activation pattern during bilateral arm raising movements

Bilateral arm raising movements have been used in brain rehabilitation for a long time. However, no study has been reported on the effect of these movements on the cerebral cortex. In this study, using functional near infrared spectroscopy（f NIRS）, we attempted to investigate cortical activation generated during bilateral arm raising movements. Ten normal subjects were recruited for this study. f NIRS was performed using an f NIRS system with 49 channels. Bilateral arm raising movements were performed in sitting position at the rate of 0.5 Hz. We measured values of oxyhemoglobin and total hemoglobin in five regions of interest： the primary sensorimotor cortex, premotor cortex, supplementary motor area, prefrontal cortex, and posterior parietal cortex. During performance of bilateral arm raising movements, oxyhemoglobin and total hemoglobin values in the primary sensorimotor cortex, premotor cortex, supplementary motor area, and prefrontal cortex were similar, but higher in these regions than those in the prefrontal cortex. We observed activation of the arm somatotopic areas of the primary sensorimotor cortex and premotor cortex in both hemispheres during bilateral arm raising movements. According to this result, bilateral arm raising movements appeared to induce large-scale neuronal activation and therefore arm raising movements would be good exercise for recovery of brain functions.

Effect of Preparation Process of Functional Polymer Active Materials on Properties of Gadolinium Ion Selective Electrode

Recently we have studied the rare earth ion-selective electrodes with active materials of the func-tional polymers and found that the process chosen for the functional polymers had an effect on the propertiesof gadolinium ion selective electrode besides the effects of their structures.1.Effect of preparation process of the grafted polymers on the properties ofgadolinium ion selective electrodesThe electrode membranes which consist of functional polymers as active materials were prepared by re-action of gadolinium chloride with the radiation grafted clmer of acrlic acid and polystyrene of which

Evidence of cortical reorganization of language networks after stroke with subacute Broca's aphasia:a blood oxygenation level dependent-functional magnetic resonance imaging study

Aphasia is an acquired language disorder that is a common consequence of stroke.The pathogenesis of the disease is not fully understood,and as a result,current treatment options are not satisfactory.Here,we used blood oxygenation level-dependent functional magnetic resonance imaging to evaluate the activation of bilateral cortices in patients with Broca＇s aphasia 1 to 3 months after stroke.Our results showed that language expression was associated with multiple brain regions in which the right hemisphere participated in the generation of language.The activation areas in the left hemisphere of aphasia patients were significantly smaller compared with those in healthy adults.The activation frequency,volumes,and intensity in the regions related to language,such as the left inferior frontal gyrus（Broca＇s area）,the left superior temporal gyrus,and the right inferior frontal gyrus（the mirror region of Broca＇s area）,were lower in patients compared with healthy adults.In contrast,activation in the right superior temporal gyrus,the bilateral superior parietal lobule,and the left inferior temporal gyrus was stronger in patients compared with healthy controls.These results suggest that the right inferior frontal gyrus plays a role in the recovery of language function in the subacute stage of stroke-related aphasia by increasing the engagement of related brain areas.

Botulinum toxin type A plus rehabilitative training for improving the motor function of the upper limbs and activities of daily life in patients with stroke and brain injury

BACKGROUND:Botulinum toxin type A(BTX-A)is mostly to be used to treat various diseases of motor disorders,whereas its effect on muscle spasm after stroke and brain injury needs further observation.OBJECTIVE:To observe the effect of BTX-A plus rehabilitative training on treating muscle spasm after stroke and brain injury.DESIGN:A randomized controlled observation.SETTINGS:Department of Rehabilitation,Department of Neurology and Department of Neurosurgery,the Second Hospital of Hebei Medical University.PARTICIPANTS:Sixty inpatients with brain injury and stroke were selected from the Department of Rehabilitation,Department of Neurology and Department of Neurosurgery,the Second Hospital of Hebei Medical University from January 2001 to August 2006.They were all confirmed by CT and MRI,and had obvious increase of spastic muscle strength in upper limbs,their Ashworth grades were grade 2 or above.The patients were randomly divided into treatment group(n=30)and control group(n=30).METHODS:①Patients in the treatment group undertook comprehensive rehabilitative trainings,and they were administrated with domestic BTX-A,which was provided by Lanzhou Institute of Biological Products,Ministry of Health(S10970037),and the muscles of flexion spasm were selected for upper limbs,20-25 IU for each site.②Patients in the treatment group were assessed before injection and at 1 and 2 weeks,1 and 3 months after injection respectively,and those in the control group were assessed at corresponding time points.The recovery of muscle spasm was assessed by modified Ashworth scale(MAS,grade 0-Ⅳ;Grade 0 for without increase of muscle strength;GradeⅣfor rigidity at passive flexion and extension);The recovery of motor function of the upper limbs was evaluated with Fugl-Meyer Assessment(FMA,total score was 226 points,including 100 for exercise,14 for balance,24 for sense,44 for joint motion,44 for pain and 66 for upper limb);The ADL were evaluated with Barthel index,the total score was 100 points,60 for mild dysfunction,60-41 for moderate dysfunction,<40 for severe dysfunction).MAIN OUTCOME MEASURES:Changes of MAS grade,FMA scores and Barthel index before and after BTX-A injection.RESULTS:All the 60 patients with brain injury and stroke were involved in the analysis of results.①FMA scores of upper limbs:The FMA score in the treatment group at 2 weeks after treatment was higher than that before treatment[(14.98±10.14),(13.10±9.28)points,P<0.05],whereas there was no significant difference at corresponding time point in the control group.The FMA scores at 1 and 3 months in the treatment group[(23.36±10.69),(35.36±11.36)points]were higher than those in the control group[(20.55±10.22),(30.33±10.96)points,P<0.01].②MAS grades of upper limbs:There were obviously fewer cases of gradeⅢin MAS at 2 weeks after treatment than before treatment in the treatment group(0,9 cases,P<0.05),whereas there was no obvious difference in the control group.There were obviously fewer cases of gradeⅢin MAS at 2 weeks and 1 month after treatment in the treatment group(0,0 case)than the control group(5,2 cases,P<0.01).③Barthel index of upper limbs:The Barthel index at 2 weeks after treatment was higher than that before treatment in the treatment group[(30.36±22.25),(28.22±26.21)points,P<0.05],whereas there was no significant difference in the control group.The Barthel indexes at 1 and 3 months after treatment in the treatment group were obviously higher than those in the control group[(20.55±10.22),(30.33±10.96)points,P<0.01].CONCLUSION:BTX-A has obvious efficacy on decreasing muscle tension after stroke and brain injury,and relieving muscle spasm;Meanwhile,the combination with rehabilitative training can effectively ameliorate the motor function of upper limbs and ADL of the patients.

EFFECT OF STRUCTURE OF FUNCTIONAL POLYMER ACTIVE MATERIALS ON PROPERTIES OF GADOLINIUM ION SELECTIVE ELECTRODE

In this paper,the functional polymeric active materials were prepared by the grafting copolymerization and their structure and properties were studied.The results show that the structure and properties of these ac- tive materials have the relative large effects on the properties of gadolinium ion selective electrodes.