Impact of dietary nutrition regimens based on body composition analysis on bone metabolism in Alzheimer’s disease patients

BACKGROUND Body composition analysis(BCA)is primarily used in the management of conditions such as obesity and endocrine disorders.However,its potential in providing nutritional guidance for patients with Alzheimer’s disease(AD)remains relatively unexplored.AIM To explore the clinical efficacy of BCA-based dietary nutrition scheme on bone metabolism in AD patients.METHODS This retrospective study included 96 patients with AD complicated by osteoporosis who were admitted to The Third Hospital of Quzhou between January 2023 and December 2024.Based on data from previous similar studies,the patients were randomly assigned to either a routine diet(RD)group(n=48)or a personalized nutrition(PN)group(n=48).The RD group received conventional dietary guidance,while the PN group received individualized diet intervention measures based on human BCA.The intervention period lasted for 12 weeks.Bone mineral density(BMD),body mass index(BMI),muscle mass,mineral content,osteocalcin,25-hydroxyvitamin D,procollagen type I N-terminal propeptide(PINP),beta C-terminal telopeptide of type I collagen(β-CTX),and serum calcium were measured and compared between the two groups before and 12 weeks after the intervention.RESULTS No significant differences were observed between groups in terms of age,sex,height,BMI,or other baseline data(P>0.05).In both groups,BMI did not show significant changes after the intervention(P>0.05),whereas muscle mass and mineral content were significantly increased(P<0.05).After the intervention,BMI in the PN group did not differ significantly from that of the RD group,but muscle mass and mineral content were significantly higher in the PN group(P<0.05).After the intervention,a higher proportion of patients in the PN group had a T score>-1 compared to the RD group(P<0.05).The mini-mental state examination(MMSE)score was similar in both groups before the intervention.However,12 weeks after the intervention,the MMSE score in the PN group was significantly higher than that in the RD group(P<0.05).In both groups,the MMSE score significantly increased 12 weeks post-intervention compared to pre-intervention levels(P<0.05).Before the intervention,the levels of osteocalcin,serum calcium,PINP,β-CTX,and 25-hydroxyvitamin D were not significantly different between the two groups(P>0.05).After 12 weeks of intervention,the PN group exhibited higher levels of osteocalcin,serum calcium,and 25-hydroxyvitamin D,as well as lower levels of PINP andβ-CTX,compared to the RD group(P<0.05).In both groups,osteocalcin,serum calcium,and 25-hydroxyvitamin D levels were significantly higher,while PINP andβ-CTX levels were significantly lower after 12 weeks of intervention compared to baseline(P<0.05).CONCLUSION The human BCA-based dietary nutrition regimen plays a crucial role in improving BMD and bone metabolism,with effects that surpass those of conventional nutrition strategies.The findings of this study provide strong evidence for the nutritional management of AD patients.

Predicting pillar stability for underground mine using Fisher discriminant analysis and SVM methods

The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability for underground mines selected from various coal and stone mines by using some index and mechanical properties, including the width, the height, the ratio of the pillar width to its height, the uniaxial compressive strength of the rock and pillar stress. The study includes four main stages： sampling, testing, modeling and assessment of the model performances. During the modeling stage, two pillar stability prediction models were investigated with FDA and SVMs methodology based on the statistical learning theory. After using 40 sets of measured data in various mines in the world for training and testing, the model was applied to other 6 data for validating the trained proposed models. The prediction results of SVMs were compared with those of FDA as well as the measured field values. The general performance of models developed in this study is close; however, the SVMs exhibit the best performance considering the performance index with the correct classification rate Prs by re-substitution method and Pcv by cross validation method. The results show that the SVMs approach has the potential to be a reliable and practical tool for determination of pillar stability for underground mines.

A Bayesian multi-model inference methodology for imprecise momentindependent global sensitivity analysis of rock structures

Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully.

Transitivity Analysis of the Energy in China’s New Era

At present,with the steady development of the global economy,more and more countries begin to pay attention to the impact of ecological environment on economic development and human society,so the ecological environment has become a global issue that cannot be ignored in today’s era.Therefore,from the perspective of the ecological philosophy of Diversity&Harmony as well as Interaction&Co-existence,this paper will conduct ecological discourse analysis on the Energy in China’s New Era based on the transitivity system of systemic-functional grammar,and use the Corpus analysis software UAM Corpus Tool 3.3x to label and make statistics on the transitivity system,aiming to explore the distribution characteristics of the transitivity system in this white paper.Through the transitivity analysis of the white paper,this study helps readers to have a deeper understanding of the positive significance contained in the white paper.To a certain extent,it enables readers at home and abroad to understand China’s stance on energy issues and the positive image of China in energy ecology.It also awaken readers’awareness of environmental protection and acquire good habits of resource conservation to be in harmony between human and nature for sustainable development.

Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.

Conceptual Analysis of Self-Management in Patients with Hypertension

Objective:To clarify the concept of self-management in hypertensive patients by analyzing the definition,attributes,and measurement tools through a literature review.Methods:An Internet-based search of the databases was conducted using Academic Search Complete,Medical Line,CINAHL,Health Source:Nursing/Scholarly Edition,and Google Scholar.In the search process,keywords or free text were combined by using Boolean operators,with the search terms“self of management”or“self-management,”“concept*analysis”or“concept*definition,”and Walker and Avant’s concept analysis method was used.Results:Analysis of relevant literature summarized the conceptual attributes of self-management in hypertensive patients as the active participation of patients in the treatment process;the presence of interaction provided by patients and health care providers;the use of certain health management tools;and the aim of maintaining and improving the health status and living capacity of hypertensive patients.Conclusion:The concept of self-management for hypertensive patients was clarified,which helps to promote the application of hypertensive self-management in clinical work and improve outcomes and quality of life for patients with hypertension.

Fault Diagnosis for Batch Processes by Improved Multi-model Fisher Discriminant Analysis

Since there are not enough fault data in historical data sets, it is very difficult to diagnose faults for batch processes. In addition, a complete batch trajectory can be obtained till the end of its operation. In order to overcome the need for estimated or filled up future unmeasured values in the online fault diagnosis, sufficiently utilize the finite information of faults, and enhance the diagnostic performance, an improved multi-model Fisher discriminant analysis is represented. The trait of the proposed method is that the training data sets are made of the current measured information and the past major discriminant information, and not only the current information or the whole batch data. An industrial typical multi-stage streptomycin fermentation process is used to test the performance of fault diagnosis of the proposed method.

Fisher discriminant analysis model and its application for prediction of classification of rockburst in deep-buried long tunnel

A Fisher discriminant analysis （FDA） model for the prediction of classification of rockburst in deep-buried long tunnel was established based on the Fisher discriminant theory and the actual characteristics of the project. First, the major factors of rockburst, such as the maximum tangential stress of the cavern wall σθ, uniaxial compressive strength σc, uniaxial tensile strength or, and the elastic energy index of rock Wet, were taken into account in the analysis. Three factors, Stress coefficient σθ/σc, rock brittleness coefficient σc/σt, and elastic energy index Wet, were defined as the criterion indices for rockburst prediction in the proposed model. After training and testing of 12 sets of measured data, the discriminant functions of FDA were solved, and the ratio of misdiscrimina- tion is zero. Moreover, the proposed model was used to predict rockbursts of Qinling tunnel along Xi＇an-Ankang railway. The results show that three forecast results are identical with the actual situation. Therefore, the prediction accuracy of the FDA model is acceptable.

Silhouettes Based Human Action Recognition in Video via Procrustes Analysis and Fisher Vector Coding

This paper proposes a framework for human action recognition based on procrustes analysis and Fisher vector coding(FVC).Firstly,we applied a pose feature extracted from silhouette image by employing Procrustes analysis and local preserving projection(LPP).Secondly,the extracted feature can preserve the discriminative shape information and local manifold structure of human pose and is invariant to translation,rotation and scaling.Finally,after the pose feature was extracted,a recognition framework based on FVC and multi-class supporting vector machine was employed to classify the human action.Experimental results on benchmarks demonstrate the effectiveness of the proposed method.

Discourse Analysis of the Belt and Road Initiative From the Perspective of Central Asian Countries: A Case Study of the Republic of Kazakhstan

This study employs Norman Fairclough’s Critical Discourse Analysis(CDA)three-dimensional model,using the Republic of Kazakhstan as a case study,to delve into the discourse construction of China’s Belt and Road Initiative(BRI)in Central Asian countries.Through detailed analysis of policy documents,media reports,and public discussions in Central Asian countries,this paper reveals how the BRI constructs specific social practices,discourse events,and textual meanings within these nations.The findings indicate that through this global development strategy,China has not only strengthened its economic ties with Central Asian countries but has also exerted profound influences on political,cultural,and social levels.

Cobalt crust recognition based on kernel Fisher discriminant analysis and genetic algorithm in reverberation environment

Recognition of substrates in cobalt crust mining areas can improve mining efficiency.Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust mining area,a method based on multiple-feature sets is proposed.Features of the target echoes are extracted by linear prediction method and wavelet analysis methods,and the linear prediction coefficient and linear prediction cepstrum coefficient are also extracted.Meanwhile,the characteristic matrices of modulus maxima,sub-band energy and multi-resolution singular spectrum entropy are obtained,respectively.The resulting features are subsequently compressed by kernel Fisher discriminant analysis(KFDA),the output features are selected using genetic algorithm(GA)to obtain optimal feature subsets,and recognition results of classifier are chosen as genetic fitness function.The advantages of this method are that it can describe the signal features more comprehensively and select the favorable features and remove the redundant features to the greatest extent.The experimental results show the better performance of the proposed method in comparison with only using KFDA or GA.

An Optimization Criterion for Generalized Marginal Fisher Analysis on Undersampled Problems

Marginal Fisher analysis （MFA） not only aims to maintain the original relations of neighboring data points of the same class but also wants to keep away neighboring data points of the different classes.MFA can effectively overcome the limitation of linear discriminant analysis （LDA） due to data distribution assumption and available projection directions.However,MFA confronts the undersampled problems.Generalized marginal Fisher analysis （GMFA） based on a new optimization criterion is presented,which is applicable to the undersampled problems.The solutions to the proposed criterion for GMFA are derived,which can be characterized in a closed form.Among the solutions,two specific algorithms,namely,normal MFA （NMFA） and orthogonal MFA （OMFA）,are studied,and the methods to implement NMFA and OMFA are proposed.A comparative study on the undersampled problem of face recognition is conducted to evaluate NMFA and OMFA in terms of classification accuracy,which demonstrates the effectiveness of the proposed algorithms.

Threshold Selection Study on Fisher Discriminant Analysis Used in Exon Prediction for Unbalanced Data Sets

In gene prediction, the Fisher discriminant analysis (FDA) is used to separate protein coding region (exon) from non-coding regions (intron). Usually, the positive data set and the negative data set are of the same size if the number of the data is big enough. But for some situations the data are not sufficient or not equal, the threshold used in FDA may have important influence on prediction results. This paper presents a study on the selection of the threshold. The eigen value of each exon/intron sequence is computed using the Z-curve method with 69 variables. The experiments results suggest that the size and the standard deviation of the data sets and the threshold are the three key elements to be taken into consideration to improve the prediction results.

Kernel-based fisher discriminant analysis for hyperspectral target detection

A new method based on kernel Fisher discriminant analysis （KFDA） is proposed for target detection of hyperspectral images. The KFDA combines kernel mapping derived from support vector machine and the classical linear Fisher discriminant analysis （LFDA）, and it possesses good ability to process nonlinear data such as hyperspectral images. According to the Fisher rule that the ratio of the between-class and within-class scatters is maximized, the KFDA is used to obtain a set of optimal discriminant basis vectors in high dimensional feature space, All pixels in the hyperspectral images are projected onto the discriminant basis vectors and the target detection is performed according to the projection result. The numerical experiments are performed on hyperspectral data with 126 bands collected by Airborne Visible/Infrared Imaging Spectrometer （AVIRIS）, Tbe experimental results show the effectiveness of the proposed detection method and prove that this method has good ability to overcome small sample size and spectral variability in the hyperspectral target detection.

On-line Batch Process Monitoring and Diagnosing Based on Fisher Discriminant Analysis

A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis （FDA） was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance, the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the resuits were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.

COMBINATION OF PITCH SYNCHRONOUS ANALYSIS AND FISHER CRITERION FOR SPEAKER IDENTIFICATION

A novel text independent speaker identification system is proposed. In the proposed system, the 12-order perceptual linear predictive cepstrum and their delta coefficients in the span of five frames are extracted from the segmented speech based on the method of pitch synchronous analysis. The Fisher ratios of the original coefficients then be calculated, and the coefficients whose Fisher ratios are bigger are selected to form the 13-dimensional feature vectors of speaker. The Gaussian mixture model is used to model the speakers. The experimental results show that the identification accuracy of the proposed system is obviously better than that of the systems based on other conventional coefficients like the linear predictive cepstral coefficients and the Mel-frequency cepstral coefficients.

BOOTSTRAP TECHNIQUE FOR ROC ANALYSIS: A STABLE EVALUATION OF FISHER CLASSIFIER PERFORMANCE

This paper presents a novel bootstrap based method for Receiver Operating Characteristic (ROC) analysis of Fisher classifier. By defining Fisher classifier’s output as a statistic, the bootstrap technique is used to obtain the sampling distributions of the outputs for the positive class and the negative class respectively. As a result, the ROC curve is a plot of all the (False Positive Rate (FPR), True Positive Rate (TPR)) pairs by varying the decision threshold over the whole range of the boot- strap sampling distributions. The advantage of this method is, the bootstrap based ROC curves are much stable than those of the holdout or cross-validation, indicating a more stable ROC analysis of Fisher classifier. Experiments on five data sets publicly available demonstrate the effectiveness of the proposed method.

Economic Analysis of Diesel Fuel Subsidy Policy in China's Fishery

In this paper,on the basis of an overview of the evolution of diesel fuel subsidy policy in China's fishery,we perform an economic analysis of the existing diesel fuel subsidy policy,and believe that it is fishing shareholders rather than fishermen who benefit most from the diesel fuel subsidy policy. The diesel fuel subsidy policy is not conducive to fishery resources protection,it will cause no fluctuation in the supply price of aquatic products,and it can not effectively increase the income of all fishermen. It is necessary to focus on subsidy method,subsidy links and subsidy level to improve diesel fuel subsidy efficiency,lower production costs,stabilize fishery production,and increase the income of fishermen.