Principal Support Research Articles

Quantitative analysis of piano performance proficiency focusing on difference between hands

Quantitative evaluation of piano performance is of interests in many fields, including music education and computational performance rendering. Previous studies utilized features extracted from audio or musical instrument digital interface (MIDI) files but did not address the difference between hands (DBH), which might be an important aspect of high-quality performance. Therefore, we investigated DBH as an important factor determining performance proficiency. To this end, 34 experts and 34 amateurs were recruited to play two excerpts on a Yamaha Disklavier. Each performance was recorded in MIDI, and handcrafted features were extracted separately for the right hand (RH) and left hand (LH). These were conventional MIDI features representing temporal and dynamic attributes of each note and computed as absolute values (e. g., MIDI velocity) or ratios between performance and corresponding scores (e. g., ratio of duration or inter-onset interval (IOI)). These note-based features were rearranged into additional features representing DBH by simple subtraction between features of both hands. Statistical analyses showed that DBH was more significant in experts than in amateurs across features. Regarding temporal features, experts pressed keys longer and faster with the RH than did amateurs. Regarding dynamic features, RH exhibited both greater values and a smoother change along melodic intonations in experts that in amateurs. Further experiments using principal component analysis (PCA) and support vector machine (SVM) verified that hand-difference features can successfully differentiate experts from amateurs according to performance proficiency. Moreover, existing note-based raw feature values (Basic features) and DBH features were tested repeatedly via 10-fold cross-validation, suggesting that adding DBH features to Basic features improved F1 scores to 93.6% (by 3.5%) over Basic features. Our results suggest that differently controlling both hands simultaneously is an important skill for pianists; therefore, DBH features should be considered in the quantitative evaluation of piano performance.

Classification of typical tree species in a karst area of Guizhou Province based on principal component analysis and support vector machine

The accurate identification of forest tree species is the basis for the utilization and protection of forest resources. In this study, we collected hyperspectral data on 3287 leaves from seven typical tree species in the Caohai Nature Reserve and Huaxi District, Guizhou Province, China. The Savitzky–Golay smoothing, baseline, normalize, standardized normal variable, and moving average methods were used to preprocess the original spectral reference after removing the abnormal values, and then the dimension was reduced by principal component analysis. Finally, the reduced data were classified by support vector machine with the linear, polynomial, radial basis function, and Sigmoid kernel functions. The results show the following: (1) the principal component analysis + support vector machine method is feasible for tree species identification, however, the recognition results of different preprocessing methods, different combinations of principal components, and different support vector machine classification methods are quite different; (2) the best combination is 20 principal components + the normalize + linear support vector machine model, achieving a classification overall accuracy of 98.97%.

Principal component based support vector machine (PC-SVM): a hybrid technique for software defect detection.

Defects are the major problems in the current situation and predicting them is also a difficult task. Researchers and scientists have developed many software defects prediction techniques to overcome this very helpful issue. But to some extend there is a need for an algorithm/method to predict defects with more accuracy, reduce time and space complexities. All the previous research conducted on the data without feature reduction lead to the curse of dimensionality. We brought up a machine learning hybrid approach by combining Principal component Analysis (PCA) and Support vector machines (SVM) to overcome the ongoing problem. We have employed PROMISE (CM1: 344 observations, KC1: 2109 observations) data from the directory of NASA to conduct our research. We split the dataset into training (CM1: 240 observations, KC1: 1476 observations) dataset and testing (CM1: 104 observations, KC1: 633 observations) datasets. Using PCA, we find the principal components for feature optimization which reduce the time complexity. Then, we applied SVM for classification due to very native qualities over traditional and conventional methods. We also employed the GridSearchCV method for hyperparameter tuning. In the proposed hybrid model we have found better accuracy (CM1: 95.2%, KC1: 86.6%) than other methods. The proposed model also presents higher evaluation in the terms of other criteria. As a limitation, the only problem with SVM is there is no probabilistic explanation for classification which may very rigid towards classifications. In the future, some other method may also introduce which can overcome this limitation and keep a soft probabilistic based margin for classification on the optimal hyperplane.

SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

The electric generator is estimated to be among the top three contributors to the failure rates and downtime of wind turbines. For this reason, in the general context of increasing interest towards effective wind turbine condition monitoring techniques, fault diagnosis of electric generators is particularly important. The objective of this study is contributing to the techniques for wind turbine generator fault diagnosis through a supervisory control and data acquisition (SCADA) analysis method. The work is organized as a real-world test-case discussion, involving electric damage to the generator of a Vestas V52 wind turbine sited in southern Italy. SCADA data before and after the generator damage have been analyzed for the target wind turbine and for reference healthy wind turbines from the same site. By doing this, it has been possible to formulate a normal behavior model, based on principal component analysis and support vector regression, for the power and for the voltages and currents of the wind turbine. It is shown that the incipience of the fault can be individuated as a change in the behavior of the residuals between model estimates and measurements. This phenomenon was clearly visible approximately two weeks before the fault. Considering the fast evolution of electrical damage, this result is promising as regards the perspectives of exploiting SCADA data for individuating electric damage with an advance that can be useful for applications in wind energy practice.

Examining Three School Systems’ Actions Linked to Improving Their Lowest-Performing Schools

ABSTRACT Given that school districts have considerable discretion over the schools they lead and serve, it follows that district actions can influence school turnaround. Using data from three districts that successfully oversaw transformation efforts in their lowest performing elementary schools, this article provides evidence on how district leaders can orchestrate improvement through supporting and supervising principals and teachers. Using an analytic premise capturing leadership practices derived from the literature, we employed first cycle coding on over 800 pages of documents. Then, using emergent coding we identified “sub practices” within each literature-derived practice. Using second cycle coding, we analyzed the extent to which district leaders work with or through others. Findings provide specifics that better convey how district leaders enact their work with schools. Findings also suggest that these districts work in facilitative roles alongside school leaders. Implications for principal supervision and support are discussed.

Handwritten dynamics classification of Parkinson’s disease through support vector machine and principal component analysis

Parkinson’s disease is a chronic neurodegenerative disease that affects the daily lives of tens of thousands of middle-aged and elderly people. The intelligent classification method of Parkinson’s disease has received extensive attention in recent years. The paper proposes a new auxiliary classification model of Parkinson’s disease based on principal component analysis and support vector machine. The model first samples and preprocesses the collected handwritten sensor data, then performs dimensionality reduction by principal component analysis, and finally inputs the dimensionality reduction data into a linear kernel support vector machine for Parkinson’s disease classification and prediction. The experiment uses 5-fold cross-validation for dataset segmentation and performance verification. The average performance results obtained on the Meander r dataset are: accuracy is 70.86%, specificity is 67.23%, sensitivity is 75.98%, and F1-Score is 69.72%, and the average performance results obtained on the Spiral dataset are: accuracy is 77.45%, specificity is 70.26%, sensitivity is 85.58%, and F1-Score is 77.10%.

Towards Understanding Change-Supportive Organisational Behaviours in China: An Investigation of the 2015 Chinese National Football Reform

This paper investigates the influences of change recipients’ supportive behaviors toward the national reform in the Chinese football sector. Qualitative data were collected through semi-structured interviews (n = 29), which were conducted with change recipients from national and local football associations and commercial football clubs. Drawing on an integrated conceptual framework, the findings suggest that the change-supportive behaviors demonstrated by the change recipients were influenced and incentivised by managerial factors (i.e., management competency, communication channels, participation in decision-making, leaders’ commitment to change, and principal support); and contextual factors (i.e., an amenable football environment and the perceived political pressure to change). Three manifestations of change-supportive behaviors were identified: a) showing understanding of the change but pessimistic about the outcome; b) supporting the change and being willing to take risks; and c) supporting the change and actively seeking alternative solutions.

In search of keeping good teachers: Mediators of teacher commitment to the profession

To better understand teacher retention, the thrust of the study was to investigate the relationships among principal support, teacher efficacy, collective efficacy and practicing teachers’ commitment to the profession. This study was designed to test the mediating role of teacher and collective efficacy beliefs on teacher commitment facilitating three mediation models. Data come from 260 public school teachers in the Washington DC area, aged between 20 and 69, who completed three questionnaires through which a predictive and mediation model of teacher commitment was tested. Teacher efficacy and collective efficacy beliefs were hypothesized to mediate the relations between the principal support and teachers’ commitment to teaching profession. Mediation regression modelling was used to test and validate these three models across the sample of K-12 teachers. Teacher collective efficacy beliefs were found to be partially mediated the relationship between principal support and teacher commitment to teaching profession whereas general teacher efficacy and personal teacher efficacy did not mediate the relationship between principal support and teachers’ commitment to the profession of teaching. The measures in this study can be used to guide professional development and measure how successful attempts are to increase teacher commitment.

CsPbI3NC-Sensitized SnO2/Multiple-Walled Carbon Nanotube Self-Assembled Nanomaterials with Highly Selective and Sensitive NH3 Sensing Performance at Room Temperature.

It is an effective strategy to enhance the sensitivity of semiconductor metal oxides (SMOs) being sensitized with CsPbI3 nanocrystals (NCs) by adjusting the heterostructure between CsPbI3NC and SMO nanomaterials. In this work, for the first time, a porous 3D multiple-walled carbon nanotube (MWCNT) network uniformly coated with SnO2 quantum nanoparticles (QNPs) and CsPbI3 nanocrystals were prepared via a simple solvent vapor-induced self-assembly method. The fabricated CsPbI3NC-SnO2QNP/MWCNT nanocomposite with vapor-induced self-assembly exhibits superior stability against the moisture as well as an excellent sensing response. The results imply that the rational design of the metal halide perovskite NC/SMO heterostructure can not only improve the stability but also meet the requirements of sensing application. The self-assembled SnO2QNP/MWCNT can facilitate the dispersion of small-sized nanoparticles and efficaciously prevent the detachment of CsPbI3NC. Compared with pristine SnO2QNP and SnO2/MWCNT sensors, the CsPbI3NC-modified SnO2QNP/MWCNT nanostructure exhibited a remarkable sensitivity of 39.2 for 0.2 ppm NH3, rapid response/recovery time of 17/18 s, and excellent selectivity towards NH3. In particular, we applied machine learning methods, including principal component analysis (PCA) and support vector machines (SVMs), to analyze the sensing performance of the CsPbI3NC-SnO2QNP/MWCNT sensor and found that the combined effects of CsPbI3NC-SnO2QNP/MWCNT heterointerfaces contributed to the improvement of selectivity of sensors. The excellent NH3 for sub-ppm level concentration is ascribed to the high sensing activity of the CsPbI3 NC-based heterojunction. This work may not only enrich the family of high-performance breath detection materials but also provide a good example for designing reasonable composite materials with specific properties in the field of metal halide perovskite/SMO heterojunctions.

Principal Support and Teacher Turnover Intention in Kuwait: Implications for Policymakers

ABSTRACT Teacher turnover is a major concern for education policymakers worldwide. This study examines the relationship between principal support and teacher turnover intention in Kuwait, focusing on the mediating role of teacher job satisfaction. Data collected from 392 teachers in public schools were analyzed using a structural equation model. The results show that principal support has an indirect effect on teacher turnover intention and is significantly mediated by teacher job satisfaction. This paper broadens the scope of principal support, discussing implications for education policymakers and providing recommendations to improve principal leadership practices and reduce teacher turnover.

Device-Independent Discrimination of Falsified Amoxicillin Capsules Using Heterogeneous Near-Infrared Spectroscopic Devices for Training and Testing of a Support Vector Machine.

The objective of this work is to demonstrate the potential of near-infrared spectroscopy for common screening of falsified medicines in the field by means of a device-independent universal discrimination approach. In order to provide a useful discrimination tool to protect people from low-quality medical products, not only is a low-cost and portable screening device necessary, but a reference library is also essential. The authors believe that a device-dependent reference library inhibits near-infrared spectroscopy from becoming a popular screening tool. In this study, to develop a device-independent method, discrimination performance is evaluated using different devices for training and testing. The training data sets for the reference library were prepared using a bench-top Fourier transform near-infrared spectrophotometer, and predictive discrimination was performed using the spectral data by a low-cost and portable wavelength dispersive near-infrared spectrophotometer. A near-infrared spectrum-based support vector machine was used for these purposes, but the screening resulted in low accuracy thought to be caused by the intrinsically device-dependent features of the spectra data. Thus, principal component analysis was performed to collect the proper components to discriminate low-quality products from standard products. The principal component score-based support vector machine was able to produce highly accurate results, identifying falsified products with no false positive cases.

A multilevel examination of teachers’ occupational commitment: the roles of job resources and disruptive student behavior

The aim of the current study was to extend knowledge of occupational commitment by examining predictors at the teacher- and school-level. Several job resources hypothesized to be positively associated with occupational commitment were examined: helpful feedback, input in decision-making, teacher collaboration, and principal discipline support. The moderating role of disruptive student behavior (which was hypothesized to negatively predict the outcome) was also examined to see whether the job resources help support teachers’ occupational commitment even when disruptive behaviors are high. With 12,955 teachers from 827 schools in four English-speaking countries (Australia, Canada, England, and the US), findings revealed at the teacher-level that helpful feedback and input in decision-making were positively associated with occupational commitment, whereas the reverse was true for disruptive student behavior. An interaction effect also showed that helpful feedback was particularly important for occupational commitment when disruptive student behavior was high. At the school-level, input in decision-making and principal discipline support were positively associated with occupational commitment and disruptive student behavior was negatively associated with the outcome. Findings were similar across the four countries.

P54.03 Multiple Microarray Analyses Identify Key Genes Associated with the Development of NSCLC from COPD

Chronic obstructive pulmonary disease (COPD) is an independent risk factor of non-small cell lung cancer (NSCLC). This study aimed to analyze the key genes and potential molecular mechanisms that are involved in the development from COPD to NSCLC. Expression profiles of COPD and NSCLC in GSE106899, GSE12472, and GSE12428 were downloaded from the Gene Expression Omnibus (GEO) database, followed by identification of the differentially expressed genes (DEGs) between COPD and NSCLC. Based on the identified DEGs, functional pathway enrichment and lung carcinogenesis-related networks analyses were performed and further visualized with Cytoscape software. Then, principal component analysis (PCA), cluster analysis, and support vector machines (SVM) verified the ability of the top modular genes to distinguish COPD from NSCLC. Additionally, the corrections between these key genes and clinical staging of NSCLC were studied using the UALCAN and HPA websites. Finally, a prognostic risk model was constructed based on multivariate Cox regression analysis. Kaplan-Meier survival curves of the top modular genes on the training and verification sets were generated. A total of 2350, 1914, and 1850 DEGs were obtained from GSE106899, GSE12472, and GSE12428 datasets, respectively. Following analysis of protein-protein interaction networks, the identified modular gene signatures containing H2AFX, MCM2, MCM3, MCM7, POLD1, and RPA1 were identified as markers for discrimination between COPD and NSCLC. The modular gene signatures were mainly enriched in the processes of DNA replication, cell cycle, mismatch repair, and others. Besides, the expression levels of these genes were significantly higher in NSCLC than in COPD, which was further verified by the immunohistochemistry. In addition, the high expression levels of H2AFX, MCM2, MCM7, and POLD1 correlate with poor prognosis of lung adenocarcinoma (LUAD). The Cox regression prognostic risk model showed the similar results and the predictive ability of this model is independent of other clinical variables. This study revealed several key modules that closely relate to NSCLC with underlying disease COPD, which provide a deeper understanding of the potential mechanisms underlying the malignant development from COPD to NSCLC. This study provides valuable prognostic factors in high-risk lung cancer patients with COPD.

Application of Deep Convolutional Neural Networks and IR Spectroscopy for the Detection of Drugs and Toxins

This paper explores the use of deep learning architectures to identify and categorize infrared spectral data with the objective of classifying drugs and toxins with a high level of accuracy. The model proposed uses a custom convolutional neural network to learn the spectrum of 192 drugs and 207 toxins. Variations in the architecture and number of blocks were iterated to find the best possible fit. A real-time implementation of such a model faces a lot of issues such as noise from different sources, spectral magnitude off-setting, and wavelength rotation. This paper aims to tackle some of these problems. Another common issue is the use of extensive pre-processing which makes it difficult to automate the entire process. We have aimed to side-step this issue with the architecture proposed. The focus is on 2 applications - detection of drugs and toxins. The data sets used are from different sources, each with its own noise factor and sampling rate. Some of the traditional models like Principal Component Analysis (PCA) and Support Vector Machines (SVM) were also tested on the datasets. The model works with minimal input data of two spectra (and three augmentations of the same) to learn the features and classifies the data from a source independent of the input. The proposed model showed a significant improvement in accuracy when compared to the other models currently in use, achieving an overall accuracy of 96.55\%. The model proposed performs extremely well with a minimal sampling rate and shows no loss in accuracy of classification even with an increase in the number of classes. The research conducted has the scope of being extended to the identification of counterfeit drugs which is a growing cause for concern. Another application could be in the detection of the presence of harmful toxins.

Data-driven fault diagnosis method for the safe and stable operation of solid oxide fuel cells system

Solid oxide fuel cell system is complex with multiple variables strongly coupled. Once a fault occurs, if it cannot be found in time, the initial minor fault may slowly evolve and spread to subsequent components. Therefore, fault diagnosis is a promising approach to guarantee the stability of the system. In this paper, the impact of air leakage and fuel starvation is investigated. To diagnose the two types of faults, a novel data-driven online fault diagnosis method based on principal component analysis and support vector machine is developed. Data comes from the entire stage of the solid oxide fuel cell system experiment. The results show that the proposed method can effectively identify the air leakage and fuel starvation fault in real time. Through comparison with traditional machine learning methods, this method shows higher accuracy and better generalization performance. Moreover, it combines prior knowledge and statistical characteristics to extract effective features, thereby reducing the calculation burden. Furthermore, with proper modifications, the proposed method can be extended to other types of solid oxide fuel cell system faults, which is significant in enhancing the reliability of the system.

Multiscale filter-based hyperspectral image classification with PCA and SVM

Abstract Hyperspectral imagery can offer images with high spectral resolution and provide a unique ability to distinguish the subtle spectral signatures of different land covers. In this paper, we develop a new algorithm for hyperspectral image classification by using principal component analysis (PCA) and support vector machines (SVM). We use PCA to reduce the dimensionality of an HSI data cube, and then perform spatial convolution with three different filters on the PCA output cube. We feed all three convolved output cubes to SVM to classify every pixel. Finally, we perform fusion on the three output maps to determine the final classification map. We conduct experiments on three widely used hyperspectral image data cubes (ie indian pines, pavia university, and salinas). Our method can improve the classification accuracy significantly when compared to several existing methods. Our novel method is relatively fast in term of CPU computational time as well.

Raman spectroscopy analysis of graphene oxide‐enhanced textiles

AbstractGraphene oxide (GO) enhanced textiles were studied using Raman spectroscopy. Compared with regular textiles, GO‐enhanced textiles show GO's signature D and G peaks at 1,350 and 1,580 cm−1; they also show GO's harmonics peaks at 2,690 and 2,925 cm−1. The intensity of GO's signature peaks has linear correlation with GO's percentage in weight. A principal component analysis (PCA) and support vector machine (SVM) model are used to classify textile samples with 98.75% accuracy.

School Principal Support in Teacher Professional Development

Turkish education system has enacted a vision of 2023 which emphasizes that school principals create and organize professional learning activities based on the needs of students and teachers. This research aimed to evaluate this vision by determining the level of support provided by school principals in teachers' professional development. It was based on a cross-sectional survey design of the quantitative method. The data collected from the 4,729 teachers working in Sanliurfa province in Turkey were analyzed. The results suggested that the school principals supported the professional development of the teachers occasionally and only 25.5% of principals supported teachers' professional development sufficiently. School principals mostly followed relevant resources to support teachers' professional development and inform teachers about innovations related to education. In addition, school principals did not adequately perform activities that may support teacher professional development such as determining individual and group development programs, organizing a professional development monitoring form for teachers, organizing educational activities outside the seminar period, receiving enough help from the experts in the area, and giving enough individual reading and research tasks. The results also indicated that the principals of high school teachers supported their professional development more than the principals working in primary and secondary schools, the principals of the classroom teachers and the principals of male teachers supported the professional development of the teachers more than the principals of the female and branch teachers and teachers’ level of education did not create any difference in the perceptions of the teachers.

A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout (Salmo Letnica)

Ohrid trout (Salamo letnica) is an endemic species of fish found in Lake Ohrid in the Former Yugoslav Republic of Macedonia (FYROM). The growth of Ohrid trout was examined in a controlled environment for a certain period, thereafter released into the lake to grow their natural population. The external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their growth. The data mining methods-based computational model can be used for fast, accurate, reliable, automatic, and improved growth monitoring procedures and classification of Ohrid trout. With this motivation, a combined approach of principal component analysis (PCA) and support vector machine (SVM) has been implemented for the visual discrimination and quantitative classification of Ohrid trout of the experimental and natural breeding and their growth stages. The PCA results in better discrimination of breeding categories of Ohrid trout at different development phases while the maximum classification accuracy of 98.33% was achieved using the combination of PCA and SVM. The classification performance of the combination of PCA and SVM has been compared to combinations of PCA and other classification methods (multilayer perceptron, naïve Bayes, random committee, decision stump, random forest, and random tree). Besides, the classification accuracy of multilayer perceptron using the original features has been studied.

Research on prediction of contact stress of acetabular lining based on principal component analysis and support vector regression

In the "worst-case" selection of hip prosthesis wear, it is necessary to calculate the contact stress of the acetabular liner. However, there are various combinations of acetabular prostheses. If calculated one by one, it will cause a large workload, a repeated and tedious calculation problem. To solve this problem, a machine learning prediction method by combining principal component analysis and support vector regressions (PCA-SVR) was established. First, the finite element method is used to analyze and calculate the contact stress of the acetabular liner in a typical combination to form a basic data set with the key size of the acetabular prosthesis as input and the contact stress as output; then, based on this data set, the PCA reduces the dimension of the input to obtain a new data set. Finally, based on this data set, SVR is used to establish the mapping model, and the optimal value of the model parameter C and is obtained by combining K-fold cross-validation and grid search method. The maximum absolute error of the prediction on the test data set is only 0.1986, the root mean square error RMSE is only 0.09309, and the R² value is 0.9426, which verifies the effectiveness of the prediction model. At the same time, the prediction performance is compared with the Ridge regression and Lasso models, which further verifies the superiority of the proposed method.