Principal Component Analysis Approach Research Articles

Beneficial Effects of Resveratrol and γ-Cyclodextrin on the Hematological and Biochemical Parameters of Healthy Wistar Rats Treated with Cisplatin: A PCA Approach.

It is well known that platinum-based antineoplastic agents, including cisplatin (CP), have side effects that limit their use. Nefrotoxicity, neurotoxicity, and hemolytic anemia are the most common side effects. There are few studies on the reduction in these effects that involves nanoencapsulation; however, almost none involve cyclodextrins (CDs). Changes in the hematological and biochemical parameters of healthy Wistar rats treated with solutions of γ-cyclodextrin/resveratrol/cisplatin (γ-CD/Rv/CP) ternary complexes are investigated for the first time. They are intraperitoneally injected with γ-CD/Rv/CP solutions containing 5 mg CP/kg.b.w. Single shots were administered to six groups of Wistar rats (six individuals for every group) using γ-CD/Rv/CP, γ-CD/CP, γ-CD/Rv complexes, as well as positive- and negative-control groups, respectively. Thirty-two hematological and biochemical parameters were evaluated from blood samples and used as input variables for the principal component analysis (PCA) discrimination of the groups. The best protection was obtained for the γ-CD/Rv/CP ternary complex, which determined closer biochemical values to the control group. These values significantly differ from those of the γ-CD/CP treated group, especially for the IP, UA, and T-Pro kidney-related biochemical parameters. This finding proves the beneficial influence of Rv during CP administration through CD-based carriers.

Incorporation of Covariates Through Principal Components in Analysis of Covariance: A Simulation Study

This study examined the use of the Principal Component Analysis (PCA) approach in incorporating covariates in the Analysis of Covariance (ANCOVA) under different experimental setups. Simulated data were used for the study, and the statistical programs were developed in R statistical software to generate the datasets for different experimental setups by varying (i) number of covariates, (ii) degree of correlation among covariates, (iii) number of treatments, (iv) difference between treatment means, and (v) number of replicates. Thousand simulations were performed for each experimental setup, and the impact of the PCA approach was assessed by means of power of the test through the proportion of rejections of H0: no difference between adjusted treatment means, in 1000 simulations. The use of PCs led to a significant gain in the power of the test in ANCOVA when there is a higher number of interrelated covariates with a limited number of observations. The impact was higher with the increase of number of covariates as well as the correlation between covariates. It can be concluded that by accommodating covariates by means of PCs, the efficiency in ANCOVA can be increased, especially if there are many covariates to be included in the analysis with a limited number of observations.

The Economic Disparity across Sri Lanka’s Districts

Globally, there is an increasing regional economic development disparity. The region’s growth depends on strong economic development. The sustainable development of Sri Lanka could be harmed by the regional economic disparity. An essential consideration in the establishment of regional development policies is the identification and magnitude of regional economic disparities. This study looks at the current state of Sri Lanka’s regional economic development disparity. This study’s methodology in this regard involved using several economic development metrics. Data for the year 2019 was acquired through authoritative sources. The multivariate analysis technique using the principal component analysis (PCA) approach has been adopted, which assigns a weight to each dimension and indicator to create composite indexes. The economic development of the western province was higher than that of the other provinces, but there was also a notable disparity between the districts of the western province, with the Colombo district having the highest economic development index. The resulting indices enable policymakers to prioritize regions for additional efforts while also assessing the state of regional disparities. The new index also makes it feasible to classify local government entities logically to support the government’s numerous policy-making and development initiatives. To calculate an index of economic disparity similar to that, the approach would apply to any nation.

Multi-Objective Optimization for the Sustainable Machining of Metal Matrix Composites Using Wire Electric Discharge Machining

Wire Electrical Discharge Machining (WEDM) is an electrothermal machining process used to produce complex profiles electrically conductive materials. In this investigation, multiobjective optimization using hybrid approach of Taguchi method (TM) and Principal Component Analysis (PCA) has been performed to find the optimal input process parameters like pulse-on time ([Formula: see text]), pulse-off time ([Formula: see text]), peak current (I), wire speed (WS) for output parameters, namely, cutting velocity (CV), power consumption (PC), and surface roughness (SR). Three different Aluminum based Metal Matrix Composites (AMMC) were fabricated using three different reinforcements i.e. 10[Formula: see text]wt.% Aluminum Oxide (Al2O3), 10[Formula: see text]wt.% Silicon Carbide (SiC), and combining 5[Formula: see text]wt.% Al2O3 with 5[Formula: see text]wt.% SiC through the stir casting process. The experiments were conducted based on the L9 orthogonal array approach of Taguchi methodology and regression equations were developed based on the experimental data for the prediction of output parameters. The results of analysis of variance (ANOVA) shows that WS and I are the two most significant factors for CV of all three developed composites whereas for SR, [Formula: see text] and I play critical role. The optimal results show that for machining of Al6061/Al2O3 and Al6061/SiC optimal input parameters are [Formula: see text][Formula: see text][Formula: see text]s, [Formula: see text][Formula: see text][Formula: see text]s, [Formula: see text][Formula: see text]A, and [Formula: see text][Formula: see text]m/s. For hybrid MMC (Al6061/Al2O3/SiC), optimized input parameters are [Formula: see text][Formula: see text][Formula: see text]s, [Formula: see text][Formula: see text][Formula: see text]s, [Formula: see text][Formula: see text]A, and [Formula: see text][Formula: see text]m/s. At optimal input parameters though the CV decreases by 1.53% the PC and SR decreases by 8.95% and 15.47%, respectively, in case of hybrid composite. The machined surface at optimal input parameters were examined by the Scanning Electron Microscope (SEM). This study will help to identify the input parameters for sustainable machining of AMMC for different reinforcements with WEDM.

Research on the Evaluation of Urban Quality in Yunnan Province Based on Principal Component Analysis Approach

Urban quality represents the spiritual taste and development quality of a city, and is the unity of the two. The level of urban quality reflects the level of the whole process of a city's planning, construction and management, and also directly affects the convenience and comfort of citizens' life, as well as relates to the agglomeration and attraction of various resource elements such as talents and industries. In other words, it is a comprehensive reflection of a city's strength, directly reflecting a city's economic development strength, urban social and cultural conditions, the beauty of the ecological environment, the level of public services and the living conditions of residents. China's economic development has entered a new period, has shifted from the stage of high-speed growth to the stage of high-quality development, and a clear understanding of the quality of the city is of great significance to high-quality development. This paper uses principal component analysis to conduct a comprehensive evaluation study of urban quality in 16 cities and towns in Yunnan Province from five dimensions: quality of urban economic development, social and cultural quality, quality of ecological environment, quality of public services, and quality of life of residents, using empirical data for the period of 2017-2021 as a sample of 16 cities and towns in Yunnan Province. The study found that although the comprehensive index of urban quality of each state and city in Yunnan Province has been steadily increasing year by year, the gap between the states and cities has not been significantly reduced. The development level of city sub-quality in each state and city in Yunnan Province is not balanced, and there are geographical differences, with a few cities, such as Kunming, having higher quality, while the quality of the majority of cities needs to be improved. The comprehensive scores of these city qualities are affected by indicators of economic development, social progress and environmental protection, and also show their own characteristics and advantages. In order to improve the quality of cities in Yunnan Province, it is recommended that policy guidance be strengthened, coordinated development among cities be promoted, investment in environmental protection be increased, and the development of social programs be advanced.

Cardiometabolic risk factor clustering in persons with spinal cord injury: A principal component analysis approach

Context/Objective To identify cardiometabolic (CM) measurements that cluster to confer increased cardiovascular disease (CVD) risk using principal component analysis (PCA) in a cohort of chronic spinal cord injury (SCI) and healthy non-SCI individuals. Approach A cross-sectional study was performed in ninety-eight non-ambulatory men with chronic SCI and fifty-one healthy non-SCI individuals (ambulatory comparison group). Fasting blood samples were obtained for the following CM biomarkers: lipid, lipoprotein particle, fasting glucose and insulin concentrations, leptin, adiponectin, and markers of inflammation. Total and central adiposity [total body fat (TBF) percent and visceral adipose tissue (VAT) percent, respectively] were obtained by dual x-ray absorptiometry (DXA). A PCA was used to identify the CM outcome measurements that cluster to confer CVD risk in SCI and non-SCI cohorts. Results Using PCA, six factor-components (FC) were extracted, explaining 77% and 82% of the total variance in the SCI and non-SCI cohorts, respectively. In both groups, FC-1 was primarily composed of lipoprotein particle concentration variables. TBF and VAT were included in FC-2 in the SCI group, but not the non-SCI group. In the SCI cohort, logistic regression analysis results revealed that for every unit increase in the FC-1 standardized score generated from the statistical software during the PCA, there is a 216% increased risk of MetS (P = 0.001), a 209% increased risk of a 10-yr. FRS ≥ 10% (P = 0.001), and a 92% increase in the risk of HOMA2-IR ≥ 2.05 (P = 0.01). Conclusion Application of PCA identified 6-FC models for the SCI and non-SCI groups. The clustering of variables into the respective models varied considerably between the cohorts, indicating that CM outcomes may play a differential role on their conferring CVD-risk in individuals with chronic SCI.

Sensor Array System Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural Network

The categorization of odors utilizing gas sensor arrays with various meatball borax concentrations has been studied. The samples included meatballs with a borax content of 0.05%, 0.10%, 0.15%, 0.20%, and 0.25% (%mm) and meatballs without any borax. Six TGS gas sensors with a baseline of 10 seconds, a detecting period of 120 seconds, and a purging period of 250 seconds make up the gas sensor array used in this work. Artificial neural networks (ANNs) and principal component analysis (PCA), which are beneficial for feature extraction and classification, are used to handle the collected data based on machine learning approaches. Two models were produced by the data analysis: model 1, which only used the PCA approach, and model 2, which only used the ANN methodology. 90.33% is the total variance value of PC from model 1. In addition, the multilayer perceptron artificial neural network (ANN-MLP) technique for model 2 yielded accuracy values of 95%.

Dynamozones are the most obvious sign of the evolution of conformational dynamics in HIV-1 protease

Proteins are not static but are flexible molecules that can adopt many different conformations. The HIV-1 protease is an important target for the development of therapies to treat AIDS, due to its critical role in the viral life cycle. We investigated several dynamics studies on the HIV-1 protease families to illustrate the significance of examining the dynamic behaviors and molecular motions for an entire understanding of their dynamics-structure–function relationships. Using computer simulations and principal component analysis approaches, the dynamics data obtained revealed that: (i) The flap regions are the most obvious sign of the evolution of conformational dynamics in HIV-1 protease; (ii) There are dynamic structural regions in some proteins that contribute to the biological function and allostery of proteins via appropriate flexibility. These regions are a clear sign of the evolution of conformational dynamics of proteins, which we call dynamozones. The flap regions are one of the most important dynamozones members that are critical for HIV-1 protease function. Due to the existence of other members of dynamozones in different proteins, we propose to consider dynamozones as a footprint of the evolution of the conformational dynamics of proteins.

Impedance Spectroscopy: A New Tool for Dairy Analysis

In this work, an impedimetric bioelectronic tongue (bioET) formed by an array of interdigitated microelectrodes sensors was developed and applied in the discrimination of chemical components usually found in milk samples. Few studies of impedimetric bioETs applied in the dairy industry can be found in the bibliography due to the complexity of the characterization of milk matrixes. Therefore, the development and study of the capabilities of multisensory systems containing different advanced materials such as nanomaterials combined with biological molecules is a new field of research.In order to develop an array of sensors with enhanced sensitivity and selectivity, sensors were modified with silver nanoparticles (AgNPs) and enzymes including galactose oxidase (GaOx), glucose oxidase (GOx), beta-galactosidase (β-gal), urease (Ure) and lipase (Lip) were immobilized on the sensors surface. Silver nanoparticles have proved to be an excellent option to enhance the performance of biosensors due to their electrochemical properties, their high electrical conductivity, and their ability to amplify bio electrochemical signals.Atomic force microscopy (AFM) was used to study the distribution of AgNPs and enzymes in the sensor surface. Silver nanoparticles showed a homogeneous arrangement along the entire surface, moreover after the enzyme immobilisation a pronounced increase in the sensors surface roughness was observed ensuring the process of the biological molecule (Figure 1.A and Figure 1.B).The impedance analysis of standard solutions of interest in milk (glucose, galactose, lactose, urea and triglycerides) demonstrated that sensors with a higher concentration of silver nanoparticles (AgNPs) in their structure reported higher sensitivities towards compounds found in milk with a reduction in the impedance module, indicating that there has been an improvement in electronic transfer due to the interaction of the nanomaterial and the electrode surface an example of the sensor behaviour is shown in Figure 1.C. Moreover, sensors that combined the nanomaterial with an enzyme showed a greater ability to differentiate between increasing concentrations of the enzymes target molecules (Figure 1.D). Taking as an example the case of glucose the sensor modified with AgNPs and glucose oxidase achieved a lower impedance module compared with the other sensors thanks to its lower resistance to electronic transfer in the sensor due to the enzymatic activity enhanced by the silver nanoparticles.Once all the standard solutions had been analysed, the chemometric analysis of their response was carried out applying a principal components analysis (PCA) approach which showed that the bioET developed was able to discriminate between the standard solutions (glucose, galactose, lactose, urea and triglycerides), resulting in five clusters according to their nature with a 79% of the variance of the original data obtained by the first two components of the system. Sugars appear in the positive region of the first component divided into three groups (lactose, galactose and glucose). In the negative region of the first component appears urea and the suspension of triglycerides that can be differentiated over the second component.Lastly, a first approach was made to the analysis of milk samples with different nutritional characteristics such as their fat, lactose and urea content. The utilization of the bioelectronic tongue showed how the different contents of molecules of interest was directly related with the response of the impedimetric sensors due to changes in the interaction of the sensors surface and the milk matrix. As it was expected, higher concentrations of fat caused an increase in the impedance module due to their interference in the electronic transfer process. Furthermore, it was confirmed that the biosensors developed showed specific responses towards milk samples with higher values of sugar and proteins contents ensuring a high degree of cross-selectivity in the system.Figure 1: A) Characterization by AFM of the sensor modified with AgNPs. B) Characterization by AFM of the biosensor with AgNPs and GOx. C) Nyquist diagram of sensors with 20 (orange), 30 (black) and 40 μl (blue) of AgNPs in 0.1M glucose. D) Nyquist diagram of the response of a sensor with AgNPs and GOx in glucose 0.1M (red), 0.01M (green) and 0.001M (grey). Figure 1

Spring water quality monitoring using multiple bioindicators from multiple collection sites

ABSTRACT The aim of this study was to examine the water quality of the Extrema River spring in a Brazilian Cerrado area. Three collection sites (P1 − P3) were sampled in the dry and rainy seasons, which are close to industries from different sectors. In the physicochemical analysis, a decrease in dissolved oxygen levels (<5 mg/L) and pH (< 6) at P3 was detected. An increase in heterotrophic bacteria count was recorded at all sites (> 500 colonies/ml). In ecotoxicological analyses, P2 and P3 exhibited toxicity using Vibrio fischeri (> 20%). In evaluating toxicity, the reduction in seed germination was significant utilizing Lactuca sativa at all locations and with Allium cepa only at P2; rootlet length was decreased at P3 on L. sativa and at all sites with A. cepa. In contrast, loss of membrane integrity and mitochondrial function of meristems was adversely affected at all locations using both L. sativa and A. cepa assays. Principal components analysis (PCA) approach indicated that seasonality apparently did not markedly interfere with the obtained data, but it is important to include more collection locations to be evaluated with multiple bioindicators in the spring region. Our data indicate the urgent need for more rigorous programs to monitor the discharge of effluents into water springs.

Ultrasonography and Sonoelastography Characteristics of Benign vs. Malignant Mesenteric Lymph Nodes in Cats: An Update.

(1) Background: Strain elastography (SE) is an ultrasound-based technique able to non-invasively assess tissue elasticity, with malignant tissues being stiffer than normal tissues. The purpose of the study was to evaluate the diagnostic performance of SE to differentiate feline mesenteric benign and malignant lymph nodes (LNs) using a multivariate approach including both SE results and B-mode ultrasound and color Doppler findings. (2) Methods: Feline enlarged mesenteric LNs were evaluated using B-mode ultrasound, color Doppler ultrasonography, and SE. Short-to-long axis ratios, borders, echogenicity, hilum, vascular flow distribution, elastographic patterns, and strain ratios were recorded. Histological and/or cytological diagnosis was available for each LN. (3) Results: A total of 88 LNs were included, 46 (52.3%) benign and 42 (47.7%) malignant; in the benign group, 40 LNs had a diagnosis of reactive hyperplasia (group A) and 6 eosinophilic sclerosing lymphadenitis (group B), while in the malignant group 42 had a diagnosis of lymphoma (group C). The principal component analysis approach showed evidence that by combining B-mode- and color Doppler-based scores with SE scores, the three groups of LNs can be accurately distinguished. (4) Conclusions: Our results demonstrate that a multivariate sonographic approach combining B-mode ultrasound, color Doppler ultrasonography, and SE can accurately distinguish benign from malignant LNs, thus helping in the clinical advice of feline patients.

Statistical process control versus deep learning for power plant condition monitoring

This study compares four models for industrial condition monitoring including a principal components analysis (PCA) approach and three deep learning models, one of which is a new, lightweight version of another. We also propose a simple attention mechanism for enchancing deep learning models with better predictions and feature importance. Two datasets are used, one simulated from the Tennessee Eastman Process, the other from two feedwater pumps at a Danish combined heat and power plant. Our final results show evidence in favour of the PCA-based approach as it has detection ability comparable to the deep learning approaches as well as faster training time, fewer hyperparameters, as well as robustness to changing operating conditions. We conclude the paper by putting into perspective the importance of building up complexity incrementally with a recommendation to start modelling with simpler and well-tested models before the adoption of more advanced, less transparent models.

Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification.

Acute lymphoblastic leukemia (ALL) is a lethal blood cancer that is characterized by an abnormal increased number of immature lymphocytes in the blood or bone marrow. For effective treatment of ALL, early assessment of the disease is essential. Manual examination of stained blood smear images is current practice for initially screening ALL. This practice is time-consuming and error-prone. In order to effectively diagnose ALL, numerous deep-learning-based computer vision systems have been developed for detecting ALL in blood peripheral images (BPIs). Such systems extract a huge number of image features and use them to perform the classification task. The extracted features may contain irrelevant or redundant features that could reduce classification accuracy and increase the running time of the classifier. Feature selection is considered an effective tool to mitigate the curse of the dimensionality problem and alleviate its corresponding shortcomings. One of the most effective dimensionality-reduction tools is principal component analysis (PCA), which maps input features into an orthogonal space and extracts the features that convey the highest variability from the data. Other feature selection approaches utilize evolutionary computation (EC) to search the feature space and localize optimal features. To profit from both feature selection approaches in improving the classification performance of ALL, in this study, a new hybrid deep-learning-based feature engineering approach is proposed. The introduced approach integrates the powerful capability of PCA and particle swarm optimization (PSO) approaches in selecting informative features from BPI mages with the power of pre-trained CNNs of feature extraction. Image features are first extracted through the feature-transfer capability of the GoogleNet convolutional neural network (CNN). PCA is utilized to generate a feature set of the principal components that covers 95% of the variability in the data. In parallel, bio-inspired particle swarm optimization is used to search for the optimal image features. The PCA and PSO-derived feature sets are then integrated to develop a hybrid set of features that are then used to train a Bayesian-based optimized support vector machine (SVM) and subspace discriminant ensemble-learning (SDEL) classifiers. The obtained results show improved classification performance for the ML classifiers trained by the proposed hybrid feature set over the original PCA, PSO, and all extracted feature sets for ALL multi-class classification. The Bayesian-optimized SVM trained with the proposed hybrid PCA-PSO feature set achieves the highest classification accuracy of 97.4%. The classification performance of the proposed feature engineering approach competes with the state of the art.

A novel hybrid machine learning model for prediction of CO2 using socio-economic and energy attributes for climate change monitoring and mitigation policies

Industrial development has contributed to carbon emissions majorly, resulting in high concentrations of greenhouse gases (GHGs) in the environment leading to climate change phenomenon. Climate change threatens people in multiple ways: threatening food security, water scarcity, frequent and extreme weather events, the spread of diseases, economic losses and migration etc. The World Health Organization (WHO) declared that climate change is the greatest threat to global health in the 21st century. Since 1970, CO2 emission has increased by about 90 per cent, making it 78 per cent of the total greenhouse gas emission. Climate change impact, carbon emission factors and social-economic attributes make the prediction of GHG emission a very complex research problem having dynamic scenarios due to a large number of factors and impacting variables. Accurate prediction of carbon emission in such a scenario makes it one of the most important and challenging research works. Artificial intelligence and machine learning approaches are increasingly being used to study complex, dynamic environmental phenomenons with high variability of time, space and other factors. The research paper proposes hybrid machine learning models for the prediction of CO2 emissions using energy and social-economic variables. The work uses energy and socioeconomic variables from 1960 to 2018 to collate them to provide a new perspective on the application of machine learning approaches in the modelling and prediction of GHG emissions. The proposed hybrid model of principal component analysis (PCA) and machine learning approaches is compared on accuracy and efficiency and performs better than other machine learning and deep learning approaches such as Linear regression variants, random forest regression (RFR), support vector regression (SVR), recurrent neural network (RNN), long short term Memory (LSTM) and Tabnet etc. The proposed hybrid model reports MAE equal to 0.0307, RMSE equal to 0.0346, MAPE equals to 5.1447 and SMAPE equal to 5.2267. In terms of efficiency of computation, the proposed hybrid machine learning model PCA+LER took 12.4 ms better than other models. This proposed research work of correlation analysis and prediction can help policymakers and governments in the mitigation and management of carbon emissions.

Market Innovation and Competitiveness of Commercial Banks in Kenya

The dynamics of change in the business environments catalysed by globalization, changing consumer preferences, and the whirlwind development of technology have exerted immense pressure on commercial banks to introduce new approaches to enter new markets and exploit existing ones. Market innovation has thus become a vital tool for survival and development. This study examined the influence of market innovation on the competitiveness of Commercial Banks in Kenya. The study was guided by the dynamic capability theory. The study utilized a positivist research philosophy. Descriptive and correlational research designs were used. The target population consisted of 175 directors and general managers of tier-one commercial banks in the following departments based in head office: Research and Development, Marketing and Communication, Customer Service, Credit, and Payments. Banks were classified into tiers through stratified sampling. Managers were classified into directors and general manager levels. Simple random sampling was then employed to select 122 respondents. Primary data was collected using closed and open-ended questionnaires. Reliability was tested by a pilot study done on Equity Bank. To ensure content validity, the research questionnaire was subjected to thorough examination with two university supervisors and two experts from the banking sector. The study adopted the Principal Component Analysis approach to test for construct validity. The study utilized descriptive and inferential analysis. Descriptive analysis included the use of frequencies, percentages, mean and standard deviation while inferential statistics employed correlation and regression analysis. Correlation was done using Pearson’s product moment while regression was conducted using simple linear regression. Data collected from open-ended questionnaires was analyzed using content analysis. Results were presented in the form of tables, graphs, and pie charts. The study findings reveal that market innovation influenced the competitiveness of commercial banks in Kenya. There was a statistically significant relationship between market innovation and competitiveness (r=0.651, p&lt;0.000) which indicated that there was a significant relationship between market innovation and competitiveness of Commercial Banks in Kenya. The study thus recommends that commercial banks should embrace market innovation as a tool to achieve competitiveness. Market innovation should be enhanced by carrying out continuous market surveys, use of account cash flows to measure clients’ financial strength, regular reviews on bank marketing strategies to match the dynamic business environment, differentiated product offering, promotional campaigns on various media platforms, use of artificial intelligence technology to target new customers and use of intermediaries to onboard new clients.

Supervised categorized principal component analysis for imagined speech classification via applying singular value decomposition on a symmetry matrix

The dimension of the feature vectors for performing the imagined speech is large. Hence, it is required to reduce their dimension as well as perform the classification simultaneously. This is a categorized principal component analysis (CatPCA) problem. However, the conventional CatPCA methods are unsupervised learning methods and the design of the unitary matrix in the conventional principal component analysis (PCA) algorithms is only based on the PCA approach. Therefore, the classification performance is limited. This paper proposes a supervised CatPCA approach for performing the imagined speech classification. Also, the objective function of an optimization problem for designing the unitary matrix is formulated based on both the PCA algorithm and the k means algorithm. To find the analytical solution of the optimization problem, a property on computing the singular value decomposition (SVD) of a symmetric matrix is employed. Then, the design of the cluster centers in the k means algorithm is formulated as a quadratic programming. Finally, the above two procedures are iterated until the algorithm converges. Since the analytical solutions of these two optimization problems can be derived analytically, the required computational power for finding the solution of the optimization problem is low. To demonstrate the effectiveness of our proposed CatPCA algorithm, the Track 3 of the International brain computer interface (BCI) Competition Database is employed as the dataset for performing the performance evaluation. The computer numerical simulation results show that our proposed CatPCA algorithm yields the higher classification accuracy compared to PCA variants and the dimension reduction related algorithms. Also, the required execution time of our proposed method is lower than those of the states of the arts methods. This demonstrates the effectiveness and the efficiency of our proposed CatPCA algorithm.

Seismic Fragility Analysis of a High-Pier Bridge under Pulse-like Ground Motion, Based on a PCA and K-Means Approach

The objective of this study is to present a novel fragility analysis method that combines principal component analysis (PCA) and the K-means clustering algorithm for a probability assessment of seismic damage in high-pier bridges undergoing pulse-like ground motions. Firstly, the method uses the correlation coefficient and the condition number as judgment indices to eliminate those seismic intensity measures (IMs) with weak correlation and multicollinearity from all 29 of the initial candidate seismic IMs, the optimal combination of IMs that satisfies the requirements for the PCA method is determined. Secondly, the method utilizes PCA to reduce the dimensionality of the optimal combination of IMs to obtain the principal components, after which the K-means algorithm is applied to classify the original group of selected pulse-like ground motions into four classes. Thirdly, a 3D finite element model of the exemplary high-pier bridge is developed via OpenSees, while incremental nonlinear dynamic time-history analyses are conducted to record the maximum cross-section curvatures of high piers under the influence of various categories of ground motions. Finally, based on the analytical procedures used in the increment dynamic analysis (IDA) method, this study develops and compares the fragility curves for the various classes of pulse-like ground motions. The results indicate the necessity of utilizing the PCA and K-means approach for classifying pulse-like ground motions in the seismic fragility analysis of high-pier bridges. This approach also significantly improves the precision and accuracy of damage probability analysis.

PCA of Running Biomechanics after 5 km between Novice and Experienced Runners.

Increased running experience appears to lower the risk of running-related injuries, but the mechanisms underlying this are unknown. Studying the biomechanics of runners with different running experiences before and after long-distance running can improve our understanding of the relationship between faulty running mechanics and injury. The purpose of the present study was to investigate if there were any differences in lower-limb biomechanics between runners after a 5 km run. Biomechanical data were collected from 15 novice and 15 experienced runners. Principal component analysis (PCA) with single-component reconstruction was used to identify variations in running biomechanics across the gait waveforms. A two-way repeated-measures ANOVA was conducted to explore the effects of runner and a 5 km run. Significant runner group differences were found for the kinematics and kinetics of lower-limb joints and ground reaction force (GRF) with respect to the magnitude across the stance phase. We found that novice runners exhibited greater changes in joint angles, joint moments, and GRFs than experienced runners regardless of the prolonged running session, and those patterns may relate to lower-limb injuries. The results of this study suggest that the PCA approach can provide unique insight into running biomechanics and injury mechanisms. The findings from the study could potentially guide training program developments and injury prevention protocols for runners with different running experiences.

A Planner's quest for identifying spatial (in)justice in local communities: A case study of urban census tracts in North Carolina, USA

For the past several decades, spatial justice has been presented as a conceptual framework to understand and address geographic inequalities. To date, most work associated with spatial justice has been qualitative and case study based. This paper seeks to explore the issue of spatial justice through the development of a Spatial Justice Index (SJI). The SJI quantitatively explores geographic based variables of urban census tracts in North Carolina to apply the underlying concepts of spatial justice in the real world. Using a principal components analysis approach, the SJI incorporates variables related to the following categories that comprise the concept of spatial justice: Public Goods, Basic Services, Cultural Goods, Economic Opportunities and Healthy Environments and are explored across the following spatial measures: spatial density, spatial proximity, spatial diversity, and spatial connectivity. The results highlight the benefits of dense, mixed use development patterns, that are well connected in achieving higher levels of spatial justice. The development of a Spatial Justice Index can be applied by urban planners and government officials across the entire Country to help communities comprehend, accept, and combat spatially injustices.

Living with water: quantitative assessment of property-level resilience to urban flooding

Purpose This paper aims to quantitively assess the resilience of residential properties to urban flooding in Port Harcourt, Nigeria, and assess whether they vary at spatially aggregated scales relative to the level of flood exposure. Design/methodology/approach The study synthesizes theoretical constructs/indicators for quantifying property level resilience, as a basis for measuring resilience. Using a two-stage purposive/stratified randomized sampling approach, 407 questionnaires were sent out to residents of 25 flood-prone areas, to solicit information on the resilience constructs as indicated by the adaptation behaviors of individual households and their property attributes. A principal component analysis approach is used as a mechanism for weighting the indicators, based on which aggregated spatial-scale resilience indices were computed for the 25 sampled areas relative to their levels of flood exposure. Findings Area 11 located in the moderate flood zone has the lowest resilience index, while Area 20 located in the high flood zone has the highest resilience index. The resilience indices for the low, moderate and high flood zone show only minimal and statistically insignificant differences indicating maladaptation even with incremental levels of flood exposure. Practical implications The approach to resilience measurement exemplifies a reproducible lens through which the concept of “living with floods” can be holistically assessed at the property level while highlighting the nexus of the social and technical dimensions. Originality/value The study moves beyond theoretical conceptualization, to empirically quantify the complex concept of property-level flood resilience.