Observation of the Effect of Video Assessment Method on the Operation Skills of Individual Nurses

Objective:To explore the application effect of video assessment method in clinical nurses’nursing operation skills.Method:To select 58 nurses who participated in the individual soldier standard in the children’s hospital in 2019 and 2020 as the research objects,among which the nurses who participated in the individual soldier standard in 2019 were the nurses who participated in the individual soldier standard in 2019 and 2020.A total of 29 people in the first batch were set as the control group,using traditional assessment methods.In 2020,the second batch of 29 nurses who participated in the individual soldier standard reached the experimental group.Using the video assessment method,there was no significant difference in general information between the two groups(P>0.05).After the assessment,the scores,coping with work pressure,and proactiveness of the two groups of research subjects were compared.Results:The experimental group’s nursing operation assessment scores,coping with work pressure,and proactiveness were significantly better than those of the control group,the difference was statistically significant(P<0.05).Conclusion:The application of the video assessment method improves the passing rate of nurses’operational skills examination,enhances nurses’initiative in learning,reduces examination pressure,and can be accurately,timely,and safely applied to clinical nursing work,which is worthy of study and promotion.

Effect of culture methods on individual variation in the growth of sea cucumber Apostichopus japonicus within a cohort and family

There is substantial individual variation in the growth rates of sea cucumber Apostiehopus japonicus individuals. This necessitates additional work to grade the seed stock and lengthens the production period. We evaluated the influence of three culture methods （free-mixed, isolated-mixed, isolated-alone） on individual variation in growth and assessed the relationship between feeding, energy conversion efficiency, and individual growth variation in individually cultured sea cucumbers. Of the different culture methods, animals grew best when reared in the isolated-mixed treatment （i.e., size classes were held separately）, though there was no difference in individual variation in growth between rearing treatment groups. The individual variation in growth was primarily attributed to genetic factors. The difference in food conversion efficiency caused by genetic differences among individuals was thought to be the origin of the variance. The level of individual growth variation may be altered by interactions among individuals and environmental heterogeneity. Our results suggest that, in addition to traditional seed grading, design of a new kind of substrate that changes the spatial distribution of sea cucumbers would effectively enhance growth and reduce individual variation in growth of sea cucumbers in culture.

Analysis of the method on industrial design guided by individual spontaneous behavior

There is a close relationship between individual spontaneous behavior and Industrial design. As individual spontaneous behavior can sent especial meaning for industrial design, there is a great necessity for creating design guided by this behavior. The design method carries out in four steps, first step is to focus on the analysis of individual action phase, pay attention to the explora- tion of the relationship between actions and tool ,and then list the importance order in the action phase, the third step is to take out the in-depth anatomical analysis of the stage, analyze the reason of the operational difficulty .and at last carries out the design concept to resolve the problem. All in all, this method revolves around the main stage of spontaneous behavior, and analyzes it in interim and solves the problem in stage.

New Individual Identification Method of Radiation Source Signal Based on Entropy Feature and SVM

In this paper,according to the defect of methods which have low identification rate in low SNR,a new individual identification method of radiation source based on information entropy feature and SVM is presented. Firstly,based on the theory of multi-resolution wavelet analysis,the wavelet power spectrum of noncooperative signal can be gotten. Secondly,according to the information entropy theory,the wavelet power spectrum entropy is defined in this paper. Therefore,the database of signal's wavelet power spectrum entropy can be built in different SNR and signal parameters. Finally,the sorting and identification model based on SVM is built for the individual identification of radiation source signal. The simulation result indicates that this method has a high individual's identification rate in low SNR,when the SNR is greater than 4 dB,the identification rate can reach 100%. Under unstable SNR conditions,when the range of SNR is between 0 dB and 24 dB,the average identification rate is more than 92. 67%. Therefore,this method has a great application value in the complex electromagnetic environment.

Reflections on Tools and Methods for Differentiated Assessments of Individual Scientists, Groups of Scientists and Scientific Journals

Requirements for research assessments There are huge differences in mission, emphasis, inherent capability, and targeted utilization of research among scientific institutions. Hence, when it comes to assessments, a one-size-fits-all approach cannot meet the goal(s) of these assessments. Probably even larger differences exist between individuals, research teams and departments.

Adaptive Boundary and Semantic Composite Segmentation Method for Individual Objects in Aerial Images

There are two types of methods for image segmentation.One is traditional image processing methods,which are sensitive to details and boundaries,yet fail to recognize semantic information.The other is deep learning methods,which can locate and identify different objects,but boundary identifications are not accurate enough.Both of them cannot generate entire segmentation information.In order to obtain accurate edge detection and semantic information,an Adaptive Boundary and Semantic Composite Segmentation method(ABSCS)is proposed.This method can precisely semantic segment individual objects in large-size aerial images with limited GPU performances.It includes adaptively dividing and modifying the aerial images with the proposed principles and methods,using the deep learning method to semantic segment and preprocess the small divided pieces,using three traditional methods to segment and preprocess original-size aerial images,adaptively selecting traditional results tomodify the boundaries of individual objects in deep learning results,and combining the results of different objects.Individual object semantic segmentation experiments are conducted by using the AeroScapes dataset,and their results are analyzed qualitatively and quantitatively.The experimental results demonstrate that the proposed method can achieve more promising object boundaries than the original deep learning method.This work also demonstrates the advantages of the proposed method in applications of point cloud semantic segmentation and image inpainting.

Assessment and Comparative Analysis of Individual Temperament and Somatotype in Young Athletes According to Iranian Traditional Medicine and Heath-Carter Method

Objectives:This study examined the hypothesis that whether any significant differences exist or not in individual temperament and somatotype components in young athletes.Methods:The cross-sectional study was carried out with 202 male athletes(age=23 years±2.7,mean±SE).They were categorized into four groups according to their temperaments by using a questionnaire.Also,the Heath-Carter method was applied to estimate the somatotype components.One-Way ANOVA followed by Scheffe’s tests was organized(p<0.05)for data analysis.Results:In this research,the highest mesomorphy,ectomorphy and endomorphy components were observed in the Blood,Yellow Bile and Phlegm temperaments,respectively with means of 6.1±0.28,3.9±0.11,and 5.9±0.32.Also,a significant difference was observed between Blood temperament and mesomorphy component(p<0.001)but Blood temperament had insignificant differences with other studied somatotype components(p>0.05).Ectomorphy and mesomorphy components significantly differ among Yellow Bile temperament(p<0.05),while an insignificant difference was found between Yellow Bile temperament and endomorphy component(p>0.05).Significant differences also were showed between Black Bile temperament and all somatotype components(p<0.05).Among Phlegm temperament and endomorphy component was a significant difference(p<0.001),but there were no significant differences between the Phlegm temperament and the other two somatotype components(p>0.05).Conclusions:Given the importance of body type in sports performance,current findings suggest that coaches should be aware of the individual temperaments which could serve as a guide to design special training schedules for athletes.

Network community identification method based on individual-centered theory

The studies show that numerous complex networks have clustering effect.It is an indispensable step to identify node clusters in network,namely community,in which nodes are closely related,and in many applications such as identification of ringleaders in anti-criminal and anti-terrorist network,efficient storage of data in Wireless Sensor Network(WSN).At present,most of community identification methods still require the specifications of the number or the scale of community by user and still can not handle overlapping nodes.In an attempt to solve these problems,a network community identification method based on utility value is proposed,which is a function of each node's clustering coefficient and degree.This method makes use of individual-centered theory for reference and can automatically determine the number of communities.In addition,this method is an overlapping community identification method in nature.It is shown through contrastive experiments that this method is more efficient than other methods based on individual-centered theory when they control the same amount of information.Finally,a research direction is proposed for network community identification method based on the individual-centered theory.

Insight Into the Separation-of-Variable Methods for the Closed-Form Solutions of Free Vibration of Rectangular Thin Plates

The separation-of-variable(SOV)methods,such as the improved SOV method,the variational SOV method,and the extended SOV method,have been proposed by the present authors and coworkers to obtain the closed-form analytical solutions for free vibration and eigenbuckling of rectangular plates and circular cylindrical shells.By taking the free vibration of rectangular thin plates as an example,this work presents the theoretical framework of the SOV methods in an instructive way,and the bisection–based solution procedures for a group of nonlinear eigenvalue equations.Besides,the explicit equations of nodal lines of the SOV methods are presented,and the relations of nodal line patterns and frequency orders are investigated.It is concluded that the highly accurate SOV methods have the same accuracy for all frequencies,the mode shapes about repeated frequencies can also be precisely captured,and the SOV methods do not have the problem of missing roots as well.

Microfluidic Chip Method for Multi-SNPs Genotyping in Individual Risk Assessment of Micronutrient Deficiency

Research on nutrigenomics has accumulated sufficient data in the past two decades that have dem on strated phe no types of single n ucleotide polymorphisms (SNPs) betwee n healthy and micronutrient-deficient populations. For instance, Zhang et al. showed that the genes MTHFR C677T, MTRR A66G, and MTR A2756G were the genetic factors resp on sible for low absorptio n and bioavailability of vitamins such as folate, B6/ and B12. It has also been reported that these nutrients are closely associated with the prevale nee of neural tube defects in newborn infants。

Physiological Markers and Reflex Pattern Progression in Individuals with Neurodevelopmental Deficits Utilizing the MNRI Method

The physiological markers of 310 individuals aged 2 through 19 were evaluated for the effects of the Masgutova Neurosensorimotor Reflex Integration Method on their four body systems: respiratory, cardiovascular, digestive, and nervous systems of individuals with neurodevelopmental deficits—cerebral palsy (CP), seizures, traumatic and acute brain injury, attention deficit and hyperactive disorders (ADD, ADHD), autism spectrum disorders, anxiety, post-trauma and post-traumatic stress disorders. We found that 53.33% of physiological markers and 66.67% of reflex patterns on the pre-test demonstrated to be poorly functioning. Both evaluation results showed statistically significant improvements after 8-days of intensive training using the Masgutova Neurosensorimotor Reflex Integration Method. Improvements according to 60.0% of the physiological markers positively correlated with functionality gains in 77.5% of reflex patterns in all four study groups compared to the control group, which did not receive the Reflex Integration training program (p-value < 0.05). The magnitude of improvement depended upon the severity of symptoms indicating the essentiality for individualized training in accordance with the diagnosis and individual neurological deficits. Results of this study show that reflex integrative techniques can lead to a reduction of stress and other negative factors blocking health homeostasis, limiting perception, and causing dysregulation in behavior and emotions, especially following traumatic events. Positive changes in physiological markers and reflex pattern functions indicate potential benefits for survival and stress resiliency through supporting neuro-physiological and neuro-psychological aspects of overall health and well-being in individuals with neurological deficits.

The Effect of the MNRI Method on Neurotransmitter Biomarkers of Individuals with Neurodevelopmental Disorders

Introduction: The MNRI (Masgutova Neurosensorimotor Reflex Integration) method was developed in 1989 in Russia and has spread world-wide to treat individuals with certain types reflex development deficits, behavior disorders, disorders of speech or language development, and learning disabilities. MNRI is based on techniques called “repatterning” or remodulation, meaning re-education, recoding the reflex nerve pathways specific for dynamic and postural reflex schemes. Objectives: Repatterning activates the extra pyramidal nervous system responsible for automatic mechanisms and processes, the extension of links between neurons, the growth of neural nets, myelination, and the creation of new nerve routing. This potential result was tested utilizing urinary measurements of the following neurotransmitters: epinephrine, norepinephrine, dopamine, DOPAC, serotonin, 5-HIAA, glycine, taurine, GABA, glutamate, PEA, and histamine. Methods: Neurological impact of the Masgutova Neurosensorimotor Reflex Method on the magnitude of changes in neurotransmitters was assessed by an external controlled and double-blind method using patients from one of the four diagnosis groups: 1) global developmental disorders;2) cerebral palsy, Traumatic Brain Injury (TBI), Acute Brain Injury (ABI), and seizures;3) ADD/ADHD;and 4) anxiety disorders. Results: The post-MNRI results in participants show a tendency for regulation of the above neurotransmitters resulting in their calming down, decrease of hypervigilance, stress resilience increase, improvements in behavioral and emotional regulation, positive emotions, and cognitive processes control. Conclusion: The application of the Masgutova Neurosensorimotor Reflex Method as a therapy modality offers a novelty paradigm for the treatment using neuro- and immune-modulation technologies presenting a non-pharmaceutical approach, based on use of neurosensorimotor reflex circuit concept.

High-Order Decoupled and Bound Preserving Local Discontinuous Galerkin Methods for a Class of Chemotaxis Models

In this paper,we explore bound preserving and high-order accurate local discontinuous Galerkin(LDG)schemes to solve a class of chemotaxis models,including the classical Keller-Segel(KS)model and two other density-dependent problems.We use the convex splitting method,the variant energy quadratization method,and the scalar auxiliary variable method coupled with the LDG method to construct first-order temporal accurate schemes based on the gradient flow structure of the models.These semi-implicit schemes are decoupled,energy stable,and can be extended to high accuracy schemes using the semi-implicit spectral deferred correction method.Many bound preserving DG discretizations are only worked on explicit time integration methods and are difficult to get high-order accuracy.To overcome these difficulties,we use the Lagrange multipliers to enforce the implicit or semi-implicit LDG schemes to satisfy the bound constraints at each time step.This bound preserving limiter results in the Karush-Kuhn-Tucker condition,which can be solved by an efficient active set semi-smooth Newton method.Various numerical experiments illustrate the high-order accuracy and the effect of bound preserving.

Structural Modal Parameter Recognition and Related Damage Identification Methods under Environmental Excitations: A Review

Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.

Hybrid Strategy of Partitioned and Monolithic Methods for Solving Strongly Coupled Analysis of Inverse and Direct Piezoelectric and Circuit Coupling

The inverse and direct piezoelectric and circuit coupling are widely observed in advanced electro-mechanical systems such as piezoelectric energy harvesters.Existing strongly coupled analysis methods based on direct numerical modeling for this phenomenon can be classified into partitioned or monolithic formulations.Each formulation has its advantages and disadvantages,and the choice depends on the characteristics of each coupled problem.This study proposes a new option:a coupled analysis strategy that combines the best features of the existing formulations,namely,the hybrid partitioned-monolithic method.The analysis of inverse piezoelectricity and the monolithic analysis of direct piezoelectric and circuit interaction are strongly coupled using a partitioned iterative hierarchical algorithm.In a typical benchmark problem of a piezoelectric energy harvester,this research compares the results from the proposed method to those from the conventional strongly coupled partitioned iterative method,discussing the accuracy,stability,and computational cost.The proposed hybrid concept is effective for coupled multi-physics problems,including various coupling conditions.

A High-Accuracy Curve Boundary Recognition Method Based on the Lattice Boltzmann Method and Immersed Moving Boundary Method

Applying numerical simulation technology to investigate fluid-solid interaction involving complex curved bound-aries is vital in aircraft design,ocean,and construction engineering.However,current methods such as Lattice Boltzmann(LBM)and the immersion boundary method based on solid ratio(IMB)have limitations in identifying custom curved boundaries.Meanwhile,IBM based on velocity correction(IBM-VC)suffers from inaccuracies and numerical instability.Therefore,this study introduces a high-accuracy curve boundary recognition method(IMB-CB),which identifies boundary nodes by moving the search box,and corrects the weighting function in LBM by calculating the solid ratio of the boundary nodes,achieving accurate recognition of custom curve boundaries.In addition,curve boundary image and dot methods are utilized to verify IMB-CB.The findings revealed that IMB-CB can accurately identify the boundary,showing an error of less than 1.8%with 500 lattices.Also,the flow in the custom curve boundary and aerodynamic characteristics of the NACA0012 airfoil are calculated and compared to IBM-VC.Results showed that IMB-CB yields lower lift and drag coefficient errors than IBM-VC,with a 1.45%drag coefficient error.In addition,the characteristic curve of IMB-CB is very stable,whereas that of IBM-VC is not.For the moving boundary problem,LBM-IMB-CB with discrete element method(DEM)is capable of accurately simulating the physical phenomena of multi-moving particle flow in complex curved pipelines.This research proposes a new curve boundary recognition method,which can significantly promote the stability and accuracy of fluid-solid interaction simulations and thus has huge applications in engineering.

Optimization of Random Feature Method in the High-Precision Regime

Machine learning has been widely used for solving partial differential equations(PDEs)in recent years,among which the random feature method(RFM)exhibits spectral accuracy and can compete with traditional solvers in terms of both accuracy and efficiency.Potentially,the optimization problem in the RFM is more difficult to solve than those that arise in traditional methods.Unlike the broader machine-learning research,which frequently targets tasks within the low-precision regime,our study focuses on the high-precision regime crucial for solving PDEs.In this work,we study this problem from the following aspects:(i)we analyze the coeffcient matrix that arises in the RFM by studying the distribution of singular values;(ii)we investigate whether the continuous training causes the overfitting issue;(ii)we test direct and iterative methods as well as randomized methods for solving the optimization problem.Based on these results,we find that direct methods are superior to other methods if memory is not an issue,while iterative methods typically have low accuracy and can be improved by preconditioning to some extent.

Application of Seminar Teaching Method Combined with Case-Based Learning and Problem-Based Learning Teaching Models in Oral Clinical Nursing Teaching

Purpose:To analyze the role of the seminar teaching method+case-based learning(CBL)teaching method+problem-based learning(PBL)teaching method in oral clinical nursing teaching.Methods:13 undergraduate nursing students who entered the department from September 2020 to September 2021 were selected as the control group,adopting conventional nursing teaching methods;13 undergraduate nursing students who entered the department from September 2021 to August 2022 were selected as the observation group,adopting the seminar+CBL+PBL teaching method.The teaching effects were compared between the two groups.Results:All assessment scores of the observation group were higher than those of the control group;all teaching satisfaction levels were higher than those of the control group(P<0.05).Conclusion:Adopting the seminar+CBL+PBL teaching method during oral clinical nursing teaching can improve nursing students’assessment scores,strengthen their core competencies,and enhance teaching satisfaction.

A Review on Sources,Extractions and Analysis Methods of a Sustainable Biomaterial:Tannins

Condensed and hydrolysable tannins are non-toxic natural polyphenols that are a commercial commodity industrialized for tanning hides to obtain leather and for a growing number of other industrial applications mainly to substitute petroleum-based products.They are a definite class of sustainable materials of the forestry industry.They have been in operation for hundreds of years to manufacture leather and now for a growing number of applications in a variety of other industries,such as wood adhesives,metal coating,pharmaceutical/medical applications and several others.This review presents the main sources,either already or potentially commercial of this forestry by-materials,their industrial and laboratory extraction systems,their systems of analysis with their advantages and drawbacks,be these methods so simple to even appear primitive but nonetheless of proven effectiveness,or very modern and instrumental.It constitutes a basic but essential summary of what is necessary to know of these sustainable materials.In doing so,the review highlights some of the main challenges that remain to be addressed to deliver the quality and economics of tannin supply necessary to fulfill the industrial production requirements for some materials-based uses.