ADJACENT MARTIX METHOD OF IDENTIFYING ISOMORPHISM TO PLANAR KINEMATIC CHAIN WITH MULTIPLE JOINTS AND HIGHER PAIRS

The adjacent matrix method for identifying isomorphism to planar kinematic chain with multiple joints and higher pairs is presented. The topological invariants of the planar kinematic chain can be calculated and compared by adjacent matrix. The quantity of calculation can be reduced effectively using the several divisions of bars and the reconfiguration of the adjacent matrix. As two structural characteristics of adjacent matrix, the number of division and division code are presented. It can be identified that two kinematic chains are isomorphic or not by comparing the structural characteristics of their adjacent matrixes using a method called matching row-to-row. This method may be applied to the planar linkage chain too. So, the methods of identifying isomorphism are unified in the planar kinematic chain that has or hasn＇t higher pairs with or without multiple joints. And it has some characters such as visual, simple and convenient for processing by computer, and so on.

A new method to identify and improve the purity of hybrid rice with herbicide resistant gene

There is a close relationship between the hy—brid rice production and seed purity.Two-linehybrid rice with higher heterosis is producedthrough the hybridization between a photo-thermo sensitive genetic male sterile(GMS) rice line and a paternal variety.But the fertili-

Re-identifying beef cattle using improved AlignedReID++

Accurate and continuous identification of individual cattle is crucial to precision farming in recent years.It is also the prerequisite to monitor the individual feed intake and feeding time of beef cattle at medium to long distances over different cameras.However,beef cattle can tend to frequently move and change their feeding position during feeding.Furthermore,the great variations in their head direction and complex environments(light,occlusion,and background)can also lead to some difficulties in the recognition,particularly for the bio-similarities among individual cattle.Among them,AlignedReID++model is characterized by both global and local information for image matching.In particular,the dynamically matching local information(DMLI)algorithm has been introduced into the local branch to automatically align the horizontal local information.In this research,the AlignedReID++model was utilized and improved to achieve the better performance in cattle re-identification(ReID).Initially,triplet attention(TA)modules were integrated into the BottleNecks of ResNet50 Backbone.The feature extraction was then enhanced through cross-dimensional interactions with the minimal computational overhead.Since the TA modules in AlignedReID++baseline model increased the model size and floating point operations(FLOPs)by 0.005 M and 0.05 G,the rank-1 accuracy and mean average precision(mAP)were improved by 1.0 percentage points and 2.94 percentage points,respectively.Specifically,the rank-1 accuracies were outperformed by 0.86 percentage points and 0.12 percentage points,respectively,compared with the convolution block attention module(CBAM)and efficient channel attention(ECA)modules,although 0.94 percentage points were lower than that of squeeze-and-excitation(SE)modules.The mAP metric values were exceeded by 0.22,0.86 and 0.12 percentage points,respectively,compared with the SE,CBAM,and ECA modules.Additionally,the Cross-Entropy Loss function was replaced with the CosFace Loss function in the global branch of baseline model.CosFace Loss and Hard Triplet Loss were jointly employed to train the baseline model for the better identification on the similar individuals.AlignedReID++with CosFace Loss was outperformed the baseline model by 0.24 and 0.92 percentage points in the rank-1 accuracy and mAP,respectively,whereas,AlignedReID++with ArcFace Loss was exceeded by 0.36 and 0.56 percentage points,respectively.The improved model with the TA modules and CosFace Loss was achieved in a rank-1 accuracy of 94.42%,rank-5 accuracy of 98.78%,rank-10 accuracy of 99.34%,mAP of 63.90%,FLOPs of 5.45 G,frames per second(FPS)of 5.64,and model size of 23.78 M.The rank-1 accuracies were exceeded by 1.84,4.72,0.76 and 5.36 percentage points,respectively,compared with the baseline model,part-based convolutional baseline(PCB),multiple granularity network(MGN),and relation-aware global attention(RGA),while the mAP metrics were surpassed 6.42,5.86,4.30 and 7.38 percentage points,respectively.Meanwhile,the rank-1 accuracy was 0.98 percentage points lower than TransReID,but the mAP metric was exceeded by 3.90 percentage points.Moreover,the FLOPs of improved model were only 0.05 G larger than that of baseline model,while smaller than those of PCB,MGN,RGA,and TransReID by 0.68,6.51,25.4,and 16.55 G,respectively.The model size of improved model was 23.78 M,which was smaller than those of the baseline model,PCB,MGN,RGA,and TransReID by 0.03,2.33,45.06,14.53 and 62.85 M,respectively.The inference speed of improved model on a CPU was lower than those of PCB,MGN,and baseline model,but higher than TransReID and RGA.The t-SNE feature embedding visualization demonstrated that the global and local features were achieve in the better intra-class compactness and inter-class variability.Therefore,the improved model can be expected to effectively re-identify the beef cattle in natural environments of breeding farm,in order to monitor the individual feed intake and feeding time.

Scientists Identify Different Developmental Trajectories for Individuals with Schizotypal Traits

Schizotypy,defined as the personality organization underlying schizophrenia and other related mental disorders,is a critical construct for a broad range of scientific disciplines.Because schizotypy can be psychometrically identified in the general population,investigating schizotypy may provide a unique

The Research about A Regional Drought Identifying System Based on Dynamic Drought Index

With the data of daily precipitation and daily evaporation,dynamic drought index was calculated and compared with the identification standard of drought grade to qualify the severity of drought.According to the dynamic drought index,a regional drought identifying system was developed for the watershed between the reach of the Yangtze River and Huaihe River in Anhui Province by using VC++ working platform and Access database.This drought identifying system would be very useful to forecast and early warn the happening of drought in this area.

Biodegradation of 2-naphthol and its metabolites by coupling Aspergillus niger with Bacillus subtilis

To explore biodegradation of 2-naphthol and its metabolites accumulated in wastewater treatment,a series of bio-degradation experiments were conducted.Two main metabolites of 2-naphthol,1,2-naphthalene-diol and 1,2-naphthoquinone,were identified by high-performance liquid chromatography with standards.Combining fungus Aspergillus niger with bacterium Bacillus subtilis in the treatment enhanced 2-naphthol degradation efficiency,lowered the accumulation of the two toxic metabolites.There were two main phases during the degradation process by the kinetic analysis：2-naphthol was first partly degraded by the fungus,producing labile and easily accumulated metabolites,and then the metabolites were mainly degraded by the bacterium,attested by the degradation processes of 1,2-naphthalene-diol and 1,2-naphthoquinone as sole source of carbon and energy.Sodium succinate,as a co-metabolic substrate,was the most suitable compound for the continuous degradation.The optimum concentration of 2-naphthol was 50 mg/L.The overall 2-naphthol degradation rate was 92%,and the COD Cr removal rate was 80% on day 10.These results indicated that high degradation rate of 2-naphthol should not be considered as the sole desirable criterion for the bioremediation of 2-naphtholcontaminated soils/wastewater.

A New (t, n) Threshold Signature Scheme Withstanding the Conspiracy Attack

A ccmspiracy attack is proposed to show that Wang-Li's scheme is instcure,because any t or more group members can impersonate other t member to sign any message withoutholding the responsibility. To avoid the conspiracy attack, this paper prestentsa new (t,n)threshold signature scheme with traceablt signers. A (t. n) threshold signature scheme allows t ormore group members of the group lo generate a signature on behalf of the group, Any less than tmembers cannot generate a valid signature, andany set of the group cannot impersonate another set ofmembers to sign any message without holding the responsibility. In case of disputes., the thresholdsignature can be opened, so that the original signers ean be traced without repealing the secretkeys.

Application of the wavelet packet and its energy spectrum to identify nugget splash during the aluminum alloys spot welding

Nugget splash during aluminum alloys spot welding has a detrimental effect on weld nugget integrity, strength and durability of the welded joints. This investigation is performed to identify nugget splash from voltage signals because these are easily accessible on-line. In the present work, we propose a novel method based on the wavelet packet transform and its energy spectrum for pattern recognition of splash signal. The result demonstrates that this novel method is more accuracy and a useful way of monitoring the spot welding quality.

A circular zone counting method of identifying a Duffing oscillator state transition and determining the critical value in weak signal detection

Identifying state transition and determining the critical value of the Duffing oscillator are crucial to indicating external signal existence and have a great influence on detection accuracy in weak signal detection. A circular zone counting （CZC） method is proposed in this paper, by combining the Duffing oscillator＇s phase trajectory feature and numerical calculation for quickly and accurately identifying state transition and determining the critical value, to realize a high- efficiency weak signal detection. Detailed model analysis and method construction of the CZC method are introduced. Numerical experiments into the reliability of the proposed CZC method compared with the maximum Lyapunov exponent （MLE） method are carried out. The CZC method is demonstrated to have better detecting ability than the MLE method, and furthermore it is simpler and clearer in calculation to extend to engineering application.

THE QUASI-BOUNDARY VALUE METHOD FOR IDENTIFYING THE INITIAL VALUE OF THE SPACE-TIME FRACTIONAL DIFFUSION EQUATION

In this article,we consider to solve the inverse initial value problem for an inhomogeneous space-time fractional diffusion equation.This problem is ill-posed and the quasi-boundary value method is proposed to deal with this inverse problem and obtain the series expression of the regularized solution for the inverse initial value problem.We prove the error estimates between the regularization solution and the exact solution by using an a priori regularization parameter and an a posteriori regularization parameter choice rule.Some numerical results in one-dimensional case and two-dimensional case show that our method is efficient and stable.

Smart Semi-active PID-ACO control strategy for tower vibration reduction in Wind Turbines with MR damper

Wind turbine technology is well known around the globe as an eco-friendly and eff ective renewable power source. However, this technology often faces reliability problems due to structural vibration. This study proposes a smart semi-active vibration control system using Magnetorheological (MR) dampers where feedback controllers are optimized with nature-inspired algorithms. Proportional integral derivative (PID) and Proportional integral (PI) controllers are designed to achieve the optimal desired force and current input for MR the damper. PID control parameters are optimized using an Ant colony optimization (ACO) algorithm. The eff ectiveness of the ACO algorithm is validated by comparing its performance with Ziegler-Nichols (Z-N) and particle swarm optimization (PSO). The placement of the MR damper on the tower is also investigated to ensure structural balance and optimal desired force from the MR damper. The simulation results show that the proposed semi-active PID-ACO control strategy can signifi cantly reduce vibration on the wind turbine tower under diff erent frequencies (i.e., 67%, 73%, 79% and 34.4% at 2 Hz, 3 Hz, 4.6 Hz and 6 Hz, respectively) and amplitudes (i.e. 50%, 58% and 67% for 50 N, 80 N, and 100 N, respectively). In this study, the simulation model is validated with an experimental study in terms of natural frequency, mode shape and uncontrolled response at the 1st mode. The proposed PID-ACO control strategy and optimal MR damper position is also implemented on a lab-scaled wind turbine tower model. The results show that the vibration reduction rate is 66% and 73% in the experimental and simulation study, respectively, at the 1st mode.

Sliding mode identifier for parameter uncertain nonlinear dynamic systems with nonlinear input

This paper presents a sliding mode (SM) based identifier to deal with the parameter identification problem for a class of parameter uncertain nonlinear dynamic systems with input nonlinearity. A sliding mode controller (SMC) is used to ensure the global reaching condition of the sliding mode for the nonlinear system; an identifier is designed to identify the uncertain parameter of the nonlinear system. A numerical example is studied to show the feasibility of the SM controller and the asymptotical convergence of the identifier.

Experimentation with Personal Identifiable Information

In this paper, actual personal identifiable information (PII) texts are analyzed to capture different types of PII sensitivities. The sensitivity of PII is one of the most important factors in determining an individual’s perception of privacy. A “gradation” of sensitivity of PII can be used in many applications, such as deciding the security level that controls access to data and developing a measure of trust when self-disclosing PII. This paper experiments with a theoretical analysis of PII sensitivity, defines its scope, and puts forward possible methodologies of gradation. A technique is proposed that can be used to develop a classification scheme of personal information depending on types of PII. Some PII expresses relationships among persons, some specifies aspects and features of a person, and some describes relationships with nonhuman objects. Results suggest that decomposing PII into privacy-based portions helps in factoring out non-PII information and focusing on a proprietor’s related information. The results also produce a visual map of the privacy sphere that can be used in approximating the sensitivity of different territories of privacy-related text. Such a map uncovers aspects of the proprietor, the proprietor’s relationship to social and physical entities, and the relationships he or she has with others.

Ophiostomatales (Ascomycota) associated with Tomicus species in southwestern China with an emphasis on Ophiostoma canum

Ophiostomatalean fungi may facilitate bark beetle colonization and reproduction.In the present study of the fungal community associated with bark beetle species belonging to Tomicus in Yunnan,China,six ophiostomatalean fungi(Ophiostoma canum,O.ips,O.tingens,Leptographium yunnanense,Leptographium sp.1 and Leptographium sp.2)were isolated from the beetles or their galleries;O.canum was the most common fungal species.The distribution of O.canum was associated with stands heavily damaged by Tomicus species and a higher percentage of valid galleries of Tomicus yunnanensis and T.minor in Yunnan pine(Pinus yunnanensis).After inoculation of Yunnan pine with the fungus,a phloem reaction zone formed and monoterpenes accumulated in the phloem.These results suggested that O.canum was pathogenic to Yunnan pine and that the wide distribution of the fungus might be benefi cial to reproduction of pine shoot beetles in Yunnan pine.However,because the reaction zone and monoterpene accumulation were mild,fungal damage of Yunnan pine might be limited.A more integrated study considering all the fungal species should be done to better understand the interactions among bark beetles,blue-stain fungi,and the tree hosts in the region.

Heterogeneity in the regenerative abilities of central nervous system axons within species: why do some neurons regenerate better than others?

Some neurons,especially in mammalian peripheral nervous system or in lower vertebrate or in vertebrate central nervous system(CNS)regenerate after axotomy,while most mammalian CNS neurons fail to regenerate.There is an emerging consensus that neurons have different intrinsic regenerative capabilities,which theoretically could be manipulated therapeutically to improve regeneration.Population-based comparisons between"good regenerating"and"bad regenerating"neurons in the CNS and peripheral nervous system of most vertebrates yield results that are inconclusive or difficult to interpret.At least in part,this reflects the great diversity of cells in the mammalian CNS.Using mammalian nervous system imposes several methodical limitations.First,the small sizes and large numbers of neurons in the CNS make it very difficult to distinguish regenerating neurons from non-regenerating ones.Second,the lack of identifiable neurons makes it impossible to correlate biochemical changes in a neuron with axonal damage of the same neuron,and therefore,to dissect the molecular mechanisms of regeneration on the level of single neurons.This review will survey the reported responses to axon injury and the determinants of axon regeneration,emphasizing non-mammalian model organisms,which are often under-utilized,but in which the data are especially easy to interpret.

Anterior segment optical coherence tomography for identifying muscle status in strabismus surgery

Anterior segment optical coherence tomography （AS-OCT） finds very few indications in the domain of strabismus surgery. Current applications remain restricted to determining limbus-muscle insertion distance and anterior segment changes after strabismus surgery. We discuss two cases of operated strabismus surgery where AS-OCT imaging helped in identifying the operated eye and/or extra ocular muscles （EOM） and thus proved instrumental in planning and management.

Discrete Methods Based on First Order Reversal Curves to Identify Preisach Model of Smart Materials

Preisach model is widely used in modeling of smart materials. Although first order reversal curves （FORCs） have often found applications in the fields of physics and geology, they are able to serve to identify Preisach model. In order to clarify the relationship between the Preisach model and the first order reversal curves, this paper is directed towards： （1） giving the reason a first order reversal curve is introduced; （2） presenting, for identifying Preisach model, two discrete methods, which are analytically based on first order reversal curves. Herein also is indicated the solution＇s uniqueness of these two identifying methods. At last, the validity of these two methods is verified by simulating a real smart actuator both methods have been applied to.

Primary culture and identify of cerebral cortical astrocytes from Sprague-Dawley rats

OBJECTIVE To establish a method for primary cultured and iden-tified Sprague-Dawley(SD) rats cerebral cortical astrocytes. METHODS Cerebral co-rtex of SD neonatal rats within 24 h was taken with stereo microscope and was cut topieces(1 mm^3),digested by Accutase and 0.1% DNAase(37℃,15 min),anddispersed cell suspension was made by mechanical method and filtered. The fibroblast cells and microglia were removed through differential adhesion and sha-ke. Passaged cells were identified by immunofluorescent with anti-Glial fibrillaryacidic protein(GFAP) antibody. RESULTS The astrocytes of rats cerebral cortex were cultured in this method,which had a large number of cells,good activity,high purity,abundant and elongated cell processes,and were interwoven into a network,showing a typical and good growth state. The third generation of the cel s comprised >95% astrocytes. CONCLUSION This simple and reliable cultivation method of astrocyte from rat cerebral tissue is established with high purity,and in a good growth condition.

Co-production of Multiple Antimicrobial Compounds by Bacillus amyloliquefaciens WY047,a Strain with Broad-Spectrum Activity

A bacterial strain WY047 was isolated from fermented grains and the bacterium was identified as Bacillus amyloliquefaciens, based on morphological, biochemical, and physiological tests, and analysis of 16S rRNA and gyr A sequences. The culture supernatant of WY047 demonstrated inhibition ofa wide spectrum ofbacteria(Gram positive and Gram negative) and fungi. Nine pairs ofprimers were designed and six genes( bmyD, fenA, hag, ituA, mrsA, and tasA) ofantimicrobial substances were detected by PCR, one ofwhich was isolated by 80% ammonium sulfate precipitation, D201 resin anion-exchange chromatography, and Sephadex G-75 filtration column. The purified peptide was estimated to be 35,207 Da and identified as flagellin by MALDI-TOF mass spectrometry. Another four antimicrobial substances were extracted with methanol and identified as iturin A, fengycin, bacillomycin D, and mersacidin through the liquid chromatography–mass spectrometry(LC–MS) method. The sixth possible peptide encoded by tas A could not be isolated in this study; however, the broader spectrum suggested huge application prospects.

Exponential Continuous Non-Parametric Neural Identifier With Predefined Convergence Velocity

This paper addresses the design of an exponential function-based learning law for artificial neural networks(ANNs)with continuous dynamics.The ANN structure is used to obtain a non-parametric model of systems with uncertainties,which are described by a set of nonlinear ordinary differential equations.Two novel adaptive algorithms with predefined exponential convergence rate adjust the weights of the ANN.The first algorithm includes an adaptive gain depending on the identification error which accelerated the convergence of the weights and promotes a faster convergence between the states of the uncertain system and the trajectories of the neural identifier.The second approach uses a time-dependent sigmoidal gain that forces the convergence of the identification error to an invariant set characterized by an ellipsoid.The generalized volume of this ellipsoid depends on the upper bounds of uncertainties,perturbations and modeling errors.The application of the invariant ellipsoid method yields to obtain an algorithm to reduce the volume of the convergence region for the identification error.Both adaptive algorithms are derived from the application of a non-standard exponential dependent function and an associated controlled Lyapunov function.Numerical examples demonstrate the improvements enforced by the algorithms introduced in this study by comparing the convergence settings concerning classical schemes with non-exponential continuous learning methods.The proposed identifiers overcome the results of the classical identifier achieving a faster convergence to an invariant set of smaller dimensions.