Principal Component Analysis Method Research Articles

Comparative chloroplast-specific SNP and nSCoT markers analysis and population structure study in kiwifruit plants.

Kiwifruit (Actinidiaceae family) is an economically important fruit tree in China and New Zealand. It is a typical dioecious plant that has undergone frequent natural hybridization, along with chromosomal ploidy diversity within the genus Actinidia, resulting in higher genetic differences and horticultural diversity between interspecific and intraspecific traits. This diversity provides a rich genetic base for breeding. China is not only the original center of speciation for the Actinidia genus but also its distribution center, housing the most domesticated species: A. chinensis var. chinensis, A. chinensis var. deliciosa, A. arguta, and A. polygama. However, there have been relatively few studies on the application of DNA markers and the genetic basis of kiwifruit plants. By combining information from chloroplast-specific SNPs and nuclear SCoT (nSCoT) markers, we can uncover complementary aspects of genetic variation, population structure, and evolutionary relationships. In this study, one chloroplast DNA (cpDNA) marker pair was selected out of nine cpDNA candidate pairs. Twenty nSCoT markers were selected and used to assess the population structure and chloroplast-specific DNA haplotype diversity in 55 kiwifruit plants (Actinidia), including 20 samples of A. chinensis var. chinensis, 22 samples of A. chinensis var. deliciosa, 11 samples of A. arguta, and two samples of A. polygama, based on morphological observations collected from China. The average genetic distance among the 55 samples was 0.26 with chloroplast-specific SNP markers and 0.57 with nSCoT markers. The Mantel test revealed a very small correlation (r = 0.21). The 55 samples were categorized into different sub-populations using Bayesian analysis, the Unweighted Pair Group Method with the Arithmetic Mean (UPGMA), and the Principal Component Analysis (PCA) method, respectively. Based on the analysis of 205 variable sites, a total of 15 chloroplast-specific DNA haplotypes were observed, contributing to a higher level of polymorphism with an Hd of 0.78. Most of the chloroplast-specific DNA haplotype diversity was distributed among populations, but significant diversity was also observed within populations. H1 was shared by 24 samples, including 12 of A. chinensis var. chinensis and 12 of A. chinensis var. deliciosa, indicating that H1 is an ancient and dominant haplotype among the 55 chloroplast-specific sequences. H2 may not have evolved further.The remaining haplotypes were rare and unique, with some appearing to be exclusive to a particular variety and often detected in single individuals. For example, the H15 haplotype was found exclusively in A. polygama. The population genetic variation explained by chloroplast-specific SNP markers has greater power than that explained by nSCoTs, with chloroplast-specific DNA haplotypes being the most efficient. Gene flow appears to be more evident between A. chinensis var. chinensis and A. chinensis var. deliciosa, as they share chloroplast-specific DNA haplotypes, In contrast, A.arguta and A. polygama possess their own characteristic haplotypes, derived from the haplotype of A. chinensis var. chinensis. Compared with A. chinensis, the A.arguta and A. polygama showed better grouping. It also seems crucial to screen out, for each type of molecular marker, especially haplotypes, the core markers of the Actinidia genus.

Performance degradation assessment of rolling bearings based on the comprehensive characteristic index and improved SVDD

Rolling bearing is one of the most critical parts of mechanical equipment, so the performance degradation assessment of rolling bearing is vital to ensure the normal operation of the whole mechanical equipment. Aiming at the problems that a single degradation characteristic can only contain limited performance degradation information of rolling bearings, a large number of redundant characteristics exist in the high-dimension characteristic set resulting in the inability to effectively mine the characteristic information of rolling bearings, and that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings, a performance degradation assessment method of rolling bearings based on the comprehensive characteristic index and improved support vector data description (SVDD) is proposed in this paper. Firstly, to solve the parameter selection problem of variational mode decomposition (VMD), a parameter-adaptive VMD method based on salp swarm algorithm based on mixed strategy (MSSSA) is proposed. Secondly, to extract the performance degradation information of rolling bearings more comprehensively and fully, the comprehensive characteristic index is proposed. Then, a kernel locality preserving projection orthogonal kernel principal component analysis (KLPPOKPCA) method is proposed to reduce the dimensionality of the extracted multi-domain characteristic set of the rolling bearing. Finally, a support vector data description with negative samples (NSSVDD) is proposed and optimized by MSSSA to solve the problem that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings and improve the detection performance of abnormal data. The experimental results show that the proposed method can accurately divide the performance degradation process of the rolling bearing. Moreover, the comparison with other methods further highlights the superiority of the proposed method in determining the point in time of early fault of the rolling bearing.

FT-Raman and FTIR spectroscopy as a tools showing marker of platinum-resistant phenomena in women suffering from ovarian cancer

Platinum-resistant phenomena in ovarian cancer is very dangerous for women suffering from this disease, because reduces the chances of complete recovery. Unfortunately, until now there are no methods to verify whether a woman with ovarian cancer is platinum-resistant. Importantly, histopathology images also were not shown differences in the ovarian cancer between platinum-resistant and platinum-sensitive tissues. Therefore, in this study, Fourier Transform InfraRed (FTIR) and FT-Raman spectroscopy techniques were used to find chemical differences between platinum-resistant and platinum-sensitive ovarian cancer tissues. Furthermore, Principal Component Analysis (PCA) and machine learning methods were performed to show if it possible to differentiate these two kind of tissues as well as to propose spectroscopy marker of platinum-resistant. Indeed, obtained results showed, that in platinum-resistant ovarian cancer tissues higher amount of phospholipids, proteins and lipids were visible, however when the ratio between intensities of peaks at 1637 cm−1 (FTIR) and at 2944 cm−1 (Raman) and every peaks in spectra was calculated, difference between groups of samples were not noticed. Moreover, structural changes visible as a shift of peaks were noticed for C–O–C, C–H bending and amide II bonds. PCA clearly showed, that PC1 can be used to differentiate platinum-resistant and platinum-sensitive ovarian cancer tissues, while two-trace two-dimensional correlation spectra (2T2D-COS) showed, that only in amide II, amide I and asymmetric CH lipids vibrations correlation between two analyzed types of tissues were noticed. Finally, machine learning algorithms showed, that values of accuracy, sensitivity and specificity were near to 100% for FTIR and around 95% for FT-Raman spectroscopy. Using decision tree peaks at 1777 cm−1, 2974 cm−1 (FTIR) and 1714 cm−1, 2817 cm−1 (FT-Raman) were proposed as spectroscopy marker of platinum-resistant.

Resource-efficient quantum principal component analysis

Principal component analysis (PCA) is an important dimensionality reduction method in machine learning and data analysis. Recently, the quantum version of PCA has been established to diagonalize quantum states. Although these quantum algorithms promise quantum advantages, they require substantial resources beyond the reach of state-of-the-art quantum technologies. This work aims to reduce resource requirements and improve the efficiency of quantum PCA. Assuming that the quantum state is accessed through a purified quantum query model and a sampling model, we propose quantum algorithms that use minimal resource requirements for ancillary qubits to reveal properties of eigenvectors and eigenvalues of a state. In particular, we show that estimating eigenvalue λ with error ε and success probability larger than λ(1−η) requests a query complexity O~(ϵ−1) and a sample complexity O~(ϵ−2η−1) , respectively. To our knowledge, our result is the first quantum speedup that achieves asymptotic linear scaling in 1/ϵ for quantum PCA. As applications, we discuss estimating the minimum relative entropy of entanglement of bipartite pure-states and performing quantum state discrimination tasks. We show that quantum speedups are maintained when the pure state has a low Schmidt number and states of discrimination have a low rank. This study opens up a new quantum PCA method for high-dimensional quantum data analysis and discusses its application in quantum information processing tasks.

Analysis of the Role of Human Capital on Economic Growth in the Beijing-Tianjin-Hebei

Abstract This paper uses some human capital related data in the Beijing-Tianjin-Hebei region as empirical data to study the impact of human capital on the economic growth of the Beijing-Tianjin-Hebei region; based on the selected data, the principal component analysis method is used to analyze health human capital, education human capital, and comprehensive human capital. Measure and compare the human capital differences of the three places in Beijing, Tianjin and Hebei. Finally, based on the economic growth model, the three places are regression analysis, and the results of different types of human capital on the three places are obtained. The analysis results show that different types of human capital measurement values in the Beijing-Tianjin-Hebei region are indeed different. In general, the human capital in the Beijing-Tianjin-Hebei region contributes more to economic growth than physical capital, and the contribution of health human capital is better than that of education human capital. Keywords: Human capital, Economic growth, Regression analysis, Principal component analysis, Health; Education.

Design and psychometric evaluation of the collaborative coping with infertility questionnaire in candidate of assisted reproductive techniques

Evaluating couples’ coping with infertility and its impact on their mental health is valuable in designing supportive programs. Since infertility is a shared problem in married life, coping with it requires collaborative coping strategies. Therefore, the aim of the present study was to design and psychometrically evaluate the collaborative coping with infertility questionnaire (CCIQ) in candidates of assisted reproductive techniques (ART). The exploratory factor analysis of a 27-item questionnaire designed based on the Likert scale in the Persian language was evaluated through the principal component analysis method in a cross-sectional study conducted on 200 couples who volunteered for ART. The cut-off point of factor loadings was considered 0.4. Furthermore, the criterion validity of the questionnaire was evaluated using a 12-item revised Fertility Adjustment Scale (R-FAS) and its relationship with the score of the CCIQ. Moreover, the internal consistency of the questionnaire was evaluated using Cronbach’s alpha correlation coefficient. In the exploratory factor analysis, 20 items with a factor loading above 0.4 were extracted under three factors. The three extracted factors with a value above one explained 43.78% of the variance of CCIQ. The factor loading of the accepted items ranged between 0.402 and 0.691. External reliability was confirmed with Cronbach’s alpha coefficient of 0.98. The relationship between CCIQ and R-FAS score was significant (p < 0.0001). The results of the study showed that the 20-item CCIQ enjoyed acceptable validity and reliability in the three dimensions of ‘dynamic interaction,’ ‘reorganizing married life goals,’ and ‘perception about infertility,’ which can be used to evaluate collaborative coping with infertility questionnaire in ART candidates.

Evaluation of resource and environmental carrying capacity in Xiong’an New Area

From four dimensions of resource, environment, ecology (green ecology) and social economy, correlation analysis method and principal component analysis method of SPSS are used to screen evaluation indexes to design a scientific and reasonable evaluation indexes system of resource and environmental carrying capacity in Xiong’an New Area in line with regional characteristics. Based on fuzzy comprehensive evaluation method, resource and environmental carrying capacity of the New Area are evaluated. The results show that: the resource and environmental carrying capacity of the New Area from 2014 to 2018 are 0.3229, 0.3556, 0.3521, 0.3859 and 0.4445 respectively, with an average annual growth rate of 8.32% and a steady growth trend. Then, some countermeasures to improve resource and environmental carrying capacity of the New Area are presented from three aspects of development and utilization of land resource, development and utilization of water resource and protection and utilization of water environment.

Exploratory insights into prefrontal cortex activity in continuous glucose monitoring: findings from a portable wearable functional near-infrared spectroscopy system

The escalating global prevalence of diabetes highlights an urgent need for advancements in continuous glucose monitoring (CGM) technologies that are non-invasive, accurate, and user-friendly. Here, we introduce a groundbreaking portable wearable functional near-infrared spectroscopy (fNIRS) system designed to monitor glucose levels by assessing prefrontal cortex (PFC) activity. Our study delineates the development and application of this novel fNIRS system, emphasizing its potential to revolutionize diabetes management by providing a non-invasive, real-time monitoring solution. Fifteen healthy university students participated in a controlled study, where we monitored their PFC activity and blood glucose levels under fasting and glucose-loaded conditions. Our findings reveal a significant correlation between PFC activity, as measured by our fNIRS system, and blood glucose levels, suggesting the feasibility of fNIRS technology for CGM. The portable nature of our system overcomes the mobility limitations of traditional setups, enabling continuous, real-time monitoring in everyday settings. We identified 10 critical features related to blood glucose levels from extensive fNIRS data and successfully correlated PFC function with blood glucose levels by constructing predictive models. Results show a positive association between fNIRS data and blood glucose levels, with the PFC exhibiting a clear response to blood glucose. Furthermore, the improved regressive rule principal component analysis (PCA) method outperforms traditional PCA in model prediction. We propose a model validation approach based on leave-one-out cross-validation, demonstrating the unique advantages of K-nearest neighbor (KNN) models. Comparative analysis with existing CGM methods reveals that our paper’s KNN model exhibits lower RMSE and MARD at 0.11 and 8.96%, respectively, and the fNIRS data were highly significant positive correlation with actual blood glucose levels (r = 0.995, p &amp;lt; 0.000). This study provides valuable insights into the relationship between metabolic states and brain activity, laying the foundation for innovative CGM solutions. Our portable wearable fNIRS system represents a significant advancement in effective diabetes management, offering a promising alternative to current technologies and paving the way for future advancements in health monitoring and personalized medicine.

Multivariate statistical methods for analysis of physicochemical and microbiological parameters of well water from the village M’Pody

The pollution of surface water and groundwater is a real public health problem that is currently receiving particular attention throughout the world. The use of water for food or hygiene purposes requires excellent physicochemical and microbiological quality. Well water is used for many purposes by the inhabitants of M’pody, a village in the Anyama district of Côte d’Ivoire. In this village, an epidemic of diarrhoea was detected in January 2020. This epidemic claimed sixty-nine (69) victims. This study aims to evaluate well water quality controls using the methods of principal component analysis (PCA), correspondence factorial analysis (CFA), analysis of variance (ANOVA) and self-organizing map (SOM) algorithm. The parameters studied were, turbidity (Tur), conductivity (Cond), hydrogen potential (pH), temperature (T), nitrate (NO3-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NO_{3^{-}}$$\\end{document}), nitrite (NO2-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NO_{2^{-}}$$\\end{document}), ammonium (NH4+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NH_{4^{+}}$$\\end{document}), phosphates (PO43-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PO_{4}^{3^{-}}$$\\end{document}), chlorides (Cl-)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(Cl^{-})$$\\end{document}, total hydrotimetric degree (DHT), sulfates (SO42-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SO_{4}^{2^{-}}$$\\end{document}), bicarbonate (HCO3-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$HCO_{3^{-}}$$\\end{document}), total alkalinity contents (TAC), escherichia coli (E.coli), thermotolerant coliforms (CTH) and enterococcus faecalis (E.faecalis). Data were collected from seventy-two (72) wells in the village during four campaigns in 2020. Physicochemical parameters were determined by electrochemical and spectrophotometric methods. Microbiological analysis was carried out using membrane filtration technique. Descriptive statistics revealed that Tur, Cond, pH and T parameters did not meet world health organization (WHO, 2017) standards. However, the parameters NO3-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NO_{3^{-}}$$\\end{document}, NO2-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NO_{2^{-}}$$\\end{document}, NH4+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NH_{4^{+}}$$\\end{document}, PO43-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PO_{4}^{3^{-}}$$\\end{document}, Cl-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Cl^{-}$$\\end{document}, DHT, TAC, SO42-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SO_{4}^{2^{-}}$$\\end{document} and HCO3-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$HCO_{3^{-}}$$\\end{document} comply with WHO standards. The results of bacteriological analyzes confirm the presence in very significant numbers of indicators of faecal contamination (CTH, E.coli and E.faecalis) in all wells. The logical explanations for faecal pollution would come from the infiltration of septic tanks located near the wells and the run-off of waste water carrying human and animal faecal matter. The diarrhea epidemic would therefore be caused by the consumption of this polluted water. PCA, FCA and hierarchical cluster analysis (HCA) were jointly employed to identify the structure of wells and deduce the principal factors controlling the parameters of these well waters. ANOVA revealed the effect of human-induced activities as the main factors influencing the physicochemical and microbiological parameters of the studied well waters. Further studies should focus on multivariate statistical techniques for effective forecasting and monitoring of emerging pollution for improved water quality.

Production of Camellia oleifera Abel Seed Oil for Injection: Extraction, Analysis, Deacidification, Decolorization, and Deodorization

Camellia seed oil (CSO), as a nutrient-rich edible oil, is widely used in foods, cosmetics, and other fields. In this work, the extraction, deacidification, decolorization, and deodorization processes of CSO were respectively optimized for meeting injectable oil standards. The results showed that the CSO extraction rate reached the highest level of 94% at optimized conditions (ultrasonic time, 31.2 min; reaction pH, 9.2; and reaction time, 3.5 h). The physicochemical indexes of CSO and 10 other vegetable oils were evaluated by the principal component analysis method, and the overall scores of vegetable oils were ranked as camellia seed oil &gt; olive oil &gt; rice oil &gt; peanut oil &gt; sesame oil &gt; corn oil &gt; soybean oil &gt; sunflower oil &gt; rapeseed oil &gt; walnut oil &gt; flaxseed oil. The physicochemical indicators of CSO were the most ideal among the 11 vegetable oils, which means that CSO is suitable as an injectable oil. Through the optimized processes of the deacidification, decolorization, and deodorization, the CSO acid value was reduced to 0.0515 mg KOH/g, the decolorization rate reached a maximum of 93.86%, and the OD430 was 0.015, meeting the requirement (≤0.045 of OD430) of injectable oil. After the deodorization process, these parameters of the refractive index, acid value, saponification value, iodine value, absorbance, unsaponifiable, moisture and volatiles, fatty acid composition, and heavy metal limits all met the pharmacopoeia standards of injectable oil in many countries and regions. The possibility of CSO as an injectable oil was first verified through refining-process optimization and nutritional index analysis, providing an important technical reference for the high-value utilization of vegetable oil.

Reversed-phase thin-layer chromatography and ultra-performance liquid chromatography/mass spectrometry to estimate the drug likeness of phosphodiesterase 10A inhibitors with phthalimide core

AbstractLipophilicity is a physicochemical parameter well known as a decisive factor for predicting the successful development of a drug. Thus, a balance between potency and physicochemical properties during medicinal chemistry optimization is needed. In this study, the lipophilicity of isoindole-1,3(2H)-dione derivatives designed as phosphodiesterase 10A (PDE10A) inhibitors was determined by chromatographic [reversed-phase thin-layer chromatography (RP-TLC) and ultra-performance liquid chromatography/mass spectrometry (UPLC/MS)] and in silico methods. To assess the correlation between the obtained lipophilicity parameters, principal component analysis (PCA) was performed. logP values obtained by chromatographic (logPRP-TLC and logPUPLC/MS) and in silico methods were compared using the PCA method. The results of PCA revealed that logPUPLC/MS and in silico clogP provided by the ChemDraw program were highly correlated. Compounds’ drug likeness was screened, and the pharmacokinetic properties were predicted. All the investigated compounds displayed drug-likeness properties, and they met the criteria of Lipinski’s rule of five, which predicted the oral bioavailability of drug candidates. Analysis of the influence of physicochemical properties on the biological activity showed that the compounds with increased potency on PDE10A had significantly higher topological polar surface area (TPSA) values. The blood‒brain barrier permeability and the hemolytic activity of model compound 18 were examined. The model compound 18 displayed no toxicity effect on erythrocytes in the hemolytic assay and good parallel artificial membrane permeability. The results showed that phthalimide compounds with benzimidazole moiety are a source of compound-targeted inhibition of PDE10A with balanced physicochemical and drug-likeness properties.

The Multi-parameter Test of Gravitational Wave Dispersion with Principal Component Analysis

In this work, we consider a conventional test of gravitational wave (GW) propagation which is based on the phenomenological parameterized dispersion relation to describe potential departures from General Relativity (GR) along the propagation of GWs. But different from tests conventionally performed previously, we vary multiple deformation coefficients simultaneously and employ the principal component analysis (PCA) method to remedy the strong degeneracy among deformation coefficients and obtain informative posteriors. The dominant PCA components can be better measured and constrained, and thus are expected to be more sensitive to potential departures from the waveform model. Using this method we analyze ten selected events and get the result that the combined posteriors of the dominant PCA parameters are consistent with GR within 99.7% credible intervals. The standard deviation of the first dominant PCA parameter is three times smaller than that of the original dispersion parameter of the leading order. However, the multi-parameter test with PCA is more sensitive to not only potential deviations from GR but also systematic errors of waveform models. The difference in results obtained by using different waveform templates hints that the demands of waveform accuracy are higher to perform the multi-parameter test with PCA. Whereas, it cannot be strictly proven that the deviation is indeed and only induced by systematic errors. It requires more thorough research in the future to exclude other possible reasons in parameter estimation and data processing.

A new scale for older resident satisfaction with residential integrated care in China: Development and validation

The factors that affect resident satisfaction with emerging residential integrated care for older people in China have been unclear due to a lack of validated instruments. Based on psychometric assessment procedures, we developed a new scale for older resident satisfaction with residential integrated care (SORSRIC). The scale was developed using a two-step process and a sample of 16 Chinese institutions and 333 residents. Exploratory and confirmatory factor analysis was used to evaluate reliability and validity. Principal component analysis and maximum variance methods were used to orthogonally rotate the items to extract seven factors. These were: resident expectations, perceived quality (physical care), perceived quality (mental care), perceived value, resident satisfaction, resident complaints, and resident loyalty. The CR value of each dimension of the pre-tested, formal scale was greater than 0.80, and the AVE value was greater than 0.50. This 24-item instrument showed good reliability and structural validity and can be applied to improve the experience and outcomes for older people in residential care.

Environmental Quality Monitoring Using Remote Sensing Ecological Index (RSEI) in Can Tho City, Vietnam.

The significance of green spaces and ecological priorities in urban areas–two major concepts and advancements in Vietnam's recent urban growth, particularly in Can Tho City–is addressed by urban planning. An ecological assessment necessitates participating in urban planning and the eco-environment protection system. This study evaluated the change in the ecological quality of Can Tho City in the period 2015 - 2020 by approaching remote sensing indicators and statistical methods. Using Landsat 8 OLI satellite data from 2015 to 2020, four indicators were calculated in this study: heat (LST), dryness (NDBSI), wetness (WET), and greenness (NDVI). These indices were then applied using the Principal Component Analysis (PCA) method to estimate the Remote Sensing Ecological Index (RSEI). According to the data, the RSEI mean was 3.66 in 2015 and 3.60 in 2020. In 2015, areas with good and very good ecological quality (EQ) accounted for 88.78% of the total natural area; by 2020, it was 84.75%. The areas with low quality increased from 1.97% in 2015 to 3.49% in 2020. This indicates a decreasing trend in ecological quality within the area. The declining trend in urban ecological quality (UEQ) at Can Tho City between 2015 and 2020 can be objectively reflected by the RSEI and the shifting proportions of the ecological level. The results of RSEI provide fundamental data on the ecological quality of the urban environment to support policymakers, managers, and authorities in implementing and developing sustainable approaches to urban ecological conservation and environmental quality preservation.

Enhancing NILM classification via robust principal component analysis dimension reduction

Non-intrusive load monitoring (NILM) techniques estimate the consumption of individual appliances in a household or facility, based on aggregated reading from a centralized meter. Usually, NILM techniques are shown to be improved when various power features and additional power quality parameters are included. However, adding power features leads to increased time complexity which is a disadvantage to real-time operation. Previous attempt to operate a principal component analysis (PCA) method to reduce the dimension of the problem managed to improve the run time but with considerably low accuracy. To this end, we utilize a robust PCA approach, to mitigate the influence of outliers in the data as a measure for improved performance. The proposed procedure achieves extraordinary results with accuracy over 96% for 600 hours long record of power quality measurements of the consumption of seven appliances from the standard AMPds dataset.

Degradation modeling and remaining useful lifetime prediction based on functional variance process

AbstractDynamic fluctuation is a common phenomenon in degradation processes. Hence, how to model it properly has a great impact on the degradation modeling as well as the remaining useful lifetime prediction. To capture the dynamic features and to avoid the risk of the model mis‐specification, a nonparametric degradation model based on functional variance process is proposed in this article. The model is composed of a unit‐specific mean trend and a degradation fluctuation which follows a stochastic process. The mean trend is estimated by the local smoother method, while the stochastic fluctuation is estimated by the functional principal component analysis method. The asymptotic properties of the estimators are proved. Also, the prediction for the remaining useful lifetime is discussed and the estimator is proved to converge in distribution. Moreover, a Bayesian scheme is developed to forecast the remaining useful lifetime for units with incomplete degradation observations. Simulation results show the superiority of the proposed method by comparing it with some existing methods. Finally, two real data sets are analyzed and used to illustrate the application of the method.

Classical vs. neural network-based PCA approaches for lossy image compression: Similarities and differences

The paper describes three lossy data compression techniques based on the principal component analysis (PCA), which are compared using the image compression task. The presented approach uses both classical PCA method based on the eigen-decomposition of the image data covariance matrix and two different neural network structures. The first neural structure is a two-layer feed-forward network with supervised learning acting as the so-called autoencoder, and the second one is a single-layered network with an unsupervised learning method commonly known as the generalized Hebbian algorithm. For each compression method mentioned above, the influence of the number of image segments (frames) and the number of eigenvalues/eigenvectors on the compression ratio and the image quality are examined using three different gray-scale test images. The paper also addresses a vital implementation issue regarding selecting appropriate data types to represent the compressed data. The paper’s main conclusion is that the classical PCA method outperforms its neural counterparts, both in terms of the decompressed image quality and the time required to perform the compression procedure. The positive aspect of using neural networks as a tool for PCA-based lossy data compression is that they do not require calculating the correlation matrix explicitly and thus can be used in online data acquisition schemes.

Functional Optical Coherence Tomography of Rat Cortical Neurovascular Activation during Monopulse Electrical Stimulation with the Microelectrode Array

This paper presents a study to evoke rat cortical functional activities, including hemodynamic and neural tissue signal changes, by monopulse electrical stimulation with a microelectrode array using functional optical coherence tomography (fOCT). Based on the principal component analysis and fuzzy clustering method (PCA-FCM), the hemodynamic response of different size blood vessels in rat cortex are analyzed, showing that the hemodynamic response of the superficial large blood vessels is more concentrated. In the regions of neural tissue where blood vessels are removed, positive significant pixels (the intensity of the pixel for five consecutive frames is greater than the average value plus triple standard deviation) and negative significant pixels (the intensity of the pixel for five consecutive frames is less than the average value minus triple standard deviation) exist, and the averaged intensity signal responds rapidly with an onset time of ~20.8 ms. Furthermore, the hemodynamic response was delayed by ~3.5 s from the neural tissue response. fOCT can provide a label-free, large-scale and depth-resolved map of cortical neurovascular activation, which is a promising technology to monitor cortical small-scale neurovascular activities.

Description of The Phenotypic Characteristics of Some Tomato Genotypes

This study was carried out in order to determine some morphological characteristics of 240 tomato genotypes at S2 level and to demonstrate the usability of these materials in breeding programs. Accordingly, the average fruit weight was between 553.11 gr and 74.83 gr. The mean pericarp thickness, carpel number, and soluble dry matter content of the genotypes were 7.38 mm, 3.6 pieces and 4.04% brix, respectively. Based on these measurements and observations, tomato genotypes were also investigated using the Cluster and Principal Component Analysis (PCA) methods. According to the PCA results, the first component explained 24.1% of the total variance, and the number of fruit carpels, average fruit weight, and fruit width were the most highly explained parameters. The second and third main components constituted 13.06% and 11.16% of the total. Based on the results of fruit characterization studies, genotypes 233, 447, 126, 22, 391, 118, 71, 100, 340, 102 were determined as superior. Overall, A high degree of morphological variation was detected among the characterized tomato genotypes.

Non-Destructive Prediction of the Mixed Mineral Pigment Content of Ancient Chinese Wall Paintings Based on Multiple Spectroscopic Techniques.

This study first developed non-destructive and accurate methods to predict the relative contents of mixed mineral pigments in ancient Chinese wall paintings using multiple spectroscopic techniques. The colorimetry, attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR), ultraviolet-visible-near-infrared (UV-Vis-NIR) spectroscopy, and Raman spectroscopy were employed. Analyses were conducted including color difference, spectral reflection, ATR FT-IR spectra, and Raman mapping for simulated samples (malachite-lazurite mixed with rabbit glue samples) before and after aging. Models were then established for predicting the relative pigment contents of samples using UV-Vis-NIR and ATR FT-IR spectral data with Beer-Lambert law, and mathematical methods comprising principal component analysis (PCA) and nonlinear curve fitting. In particular, PCA and empty modeling methods combined with non-negative partial least squares were developed to predict the relative pigment contents based on Raman mapping data. The results demonstrated that approaches comprising PCA, mathematical model, and empty modeling based on the spectral data were effective at predicting the relative pigment contents. The predicted results obtained using the mathematical model based on UV-Vis-NIR spectra had an error of about 2%, and the best prediction based on ATR FT-IR spectra had an error of <3.6% at 1041 cm-1. The errors for the predictions using PCA and empty modeling based on Raman mapping data were 0.01-9.30% and 0.28-7.15%, respectively. However, the predicted relative pigment contents obtained based on ATR FT-IR data combined with the Beer-Lambert law had higher errors. The findings of this study confirm the strong feasibility of using spectroscopic techniques for quantitatively analyzing mixed mineral pigments.