Principal Component
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2021 ◽  
Vol 218 ◽  
pp. 104437
Hiromasa Kaneko

2022 ◽  
Vol 12 (1) ◽  
pp. 1-15
Sanjana Tomer ◽  
Ketna Khanna ◽  
Sapna Gambhir ◽  
Mohit Gambhir

Parkinson disease (PD) is a neurological disorder where the dopaminergic neurons experience deterioration. It is caused from the death of the dopamine neurons present in the substantia nigra i.e., the mid part of the brain. The symptoms of this disease emerge slowly, the onset of the earlier stages shows some non-motor symptoms and with time motor symptoms can also be gauged. Parkinson is incurable but can be treated to improve the condition of the sufferer. No definite method for diagnosing PD has been concluded yet. However, researchers have suggested their own framework out of which MRI gave better results and is also a non-invasive method. In this study, the MRI images are used for extracting the features. For performing the feature extraction techniques Gray Level Co-occurrence Matrix and Principal Component Analysis are performed and are analysed. Feature extraction reduces the dimensionality of data. It aims to reduce the feature of data by generating new features from the original one.

Dr. K. B. V. Brahma Rao ◽  
Dr. R Krishnam Raju Indukuri ◽  
Dr. Suresh Varma Penumatsa ◽  
Dr. M. V. Rama Sundari ◽  

The objective of comparing various dimensionality techniques is to reduce feature sets in order to group attributes effectively with less computational processing time and utilization of memory. The various reduction algorithms can decrease the dimensionality of dataset consisting of a huge number of interrelated variables, while retaining the dissimilarity present in the dataset as much as possible. In this paper we use, Standard Deviation, Variance, Principal Component Analysis, Linear Discriminant Analysis, Factor Analysis, Positive Region, Information Entropy and Independent Component Analysis reduction algorithms using Hadoop Distributed File System for massive patient datasets to achieve lossless data reduction and to acquire required knowledge. The experimental results demonstrate that the ICA technique can efficiently operate on massive datasets eliminates irrelevant data without loss of accuracy, reduces storage space for the data and also the computation time compared to other techniques.


Undertaking innovation involves a range of different activities from ideation to the commercialisation of innovations. Each activity may have very different resources and organisational requirements, however, most prior studies treat innovation as a single un-differentiated activity. Here, using new survey data for professional service firms (PSFs) in the UK, we are able to examine separately how a range of organisational work practices influence success in ideation and commercialisation. In particular, we use principal component analysis (PCA) to identify and compare the benefits of four groups of organisational work practices relating to strategy & information sharing, recruitment & training, work flexibility & discretion and culture & leadership. Strong contrasts emerge between those work practices that are important for success in ideation and commercialisation. Work practices linked to culture & leadership are important for ideation activities, while strategy &information sharing practices are more strongly associated with commercialisation success. The results suggest clear managerial implications depending on the priority

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Kai Wang ◽  
Kangnan Li ◽  
Feng Du

The intensity and depth of China’s coal mining are increasing, and the risk of coal-gas compound dynamic disaster is prominent, which seriously restricts the green, safe, and efficient mining of China’s coal resources. How to accurately predict the risk of disasters is an important basis for disaster prevention and control. In this paper, the Pingdingshan No. 8 coal mine is taken as the research object, and the grey relational analysis (GRA), principal component analysis (PCA), and BP neural network are combined to predict the coal-gas compound dynamic disaster. First, the weights of 13 influencing factors are sorted and screened by grey relational analysis. Next, principal component analysis is carried out on the influencing factors with high weight value to extract common factors. Then, the common factor is used as the input parameter of BP neural network to train the previous data. Finally, the coal-gas compound dynamic disaster prediction model based on GRA-PCA-BP neural network is established. After verification, the model can effectively predict the occurrence of coal-gas compound dynamic disaster. The prediction results are consistent with the actual situation of the coal mine with high accuracy and practicality. This work is of great significance to ensure the safe and efficient production of deep mines.

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1006
Christina Gugerell ◽  
Takeshi Sato ◽  
Christine Hvitsand ◽  
Daichi Toriyama ◽  
Nobuhiro Suzuki ◽  

While food production and consumption processes worldwide are characterized by geographical and social distance, alternative food networks aim to reconnect producers and consumers. Our study proposes a framework to distinguish multiple dimensions of proximity in the context of Community Supported Agriculture (a type of alternative food network) and to quantitatively evaluate them. In a principal component analysis, we aggregated various detailed proximity items from a multinational survey using principal component analysis and examined their relationship with the attractiveness of Community Supported Agriculture in a multiple regression analysis. Our findings highlight the importance of relational proximity and thus of increasing trust, collaboration, and the sharing of values and knowledge within and across organizations in the food system. Rather than focusing on spatial proximity, increasing relational proximity might support alternative food networks, such as Community Supported Agriculture.

Plants ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2171
Gianluca Gilardoni ◽  
Mayra Montalván ◽  
Marjorie Vélez ◽  
Omar Malagón

The traditional Ecuadorian spice Ishpingo, characterized by a strong cinnamon-like aroma, is constituted by the dry cupules of Amazonian species Ocotea quixos. Nevertheless, bark and leaves also present aromatic properties and are sometimes used as substitutes. In the present study, the essential oils, distilled from these morphological structures, are comparatively analyzed for their chemical and enantiomeric compositions. A total of 88 components were identified with 2 orthogonal GC columns, whereas 79, corresponding to more than 94%, were also quantified with at least 1 column. Major compounds were (E)-methyl cinnamate in cupules (35.9–34.2%), (E)-cinnamaldehyde in bark (44.7–47.0%), and (E)-cinnamyl acetate (46.0–50.4%) in leaves. For what concerns the enantioselective analysis, 10 chiral terpenes and terpenoids were detected, of which 6 were present as enantiomeric pairs in at least 1 essential oil, the others being enantiomerically pure. Both quantitative and enantioselective analyses were submitted to Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), where their results confirmed significative difference among the three products.

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0254058
Xiaofang Xue ◽  
Ailing Zhao ◽  
Yongkang Wang ◽  
Haiyan Ren ◽  
Junjie Du ◽  

The composition and content of phenolic acids and flavonoids among the different varieties, development stages, and tissues of Chinese jujube (Ziziphus jujuba Mill.) were systematically examined using ultra-high-performance liquid chromatography to provide a reference for the evaluation and selection of high-value resources. Five key results were identified: (1) Overall, 13 different phenolic acids and flavonoids were detected from among the 20 excellent jujube varieties tested, of which 12 were from the fruits, 11 from the leaves, and 10 from the stems. Seven phenolic acids and flavonoids, including (+)-catechin, rutin, quercetin, luteolin, spinosin, gallic acid, and chlorogenic acid, were detected in all tissues. (2) The total and individual phenolic acids and flavonoids contents significantly decreased during fruit development in Ziziphus jujuba cv.Hupingzao. (3) The total phenolic acids and flavonoids content was the highest in the leaves of Ziziphus jujuba cv.Hupingzao, followed by the stems and fruits with significant differences among the content of these tissues. The main composition of the tissues also differed, with quercetin and rutin present in the leaves; (+)-catechin and rutin in the stems; and (+)-catechin, epicatechin, and rutin in the fruits. (4) The total content of phenolic acid and flavonoid ranged from 359.38 to 1041.33 μg/g FW across all examined varieties, with Ziziphus jujuba cv.Jishanbanzao having the highest content, and (+)-catechin as the main composition in all 20 varieties, followed by epicatechin, rutin, and quercetin. (5) Principal component analysis showed that (+)-catechin, epicatechin, gallic acid, and rutin contributed to the first two principal components for each variety. Together, these findings will assist with varietal selection when developing phenolic acids and f lavonoids functional products.

David Boe ◽  
Alexandra A. Portnova-Fahreeva ◽  
Abhishek Sharma ◽  
Vijeth Rai ◽  
Astrini Sie ◽  

We seek to use dimensionality reduction to simplify the difficult task of controlling a lower limb prosthesis. Though many techniques for dimensionality reduction have been described, it is not clear which is the most appropriate for human gait data. In this study, we first compare how Principal Component Analysis (PCA) and an autoencoder on poses (Pose-AE) transform human kinematics data during flat ground and stair walking. Second, we compare the performance of PCA, Pose-AE and a new autoencoder trained on full human movement trajectories (Move-AE) in order to capture the time varying properties of gait. We compare these methods for both movement classification and identifying the individual. These are key capabilities for identifying useful data representations for prosthetic control. We first find that Pose-AE outperforms PCA on dimensionality reduction by achieving a higher Variance Accounted For (VAF) across flat ground walking data, stairs data, and undirected natural movements. We then find in our second task that Move-AE significantly outperforms both PCA and Pose-AE on movement classification and individual identification tasks. This suggests the autoencoder is more suitable than PCA for dimensionality reduction of human gait, and can be used to encode useful representations of entire movements to facilitate prosthetic control tasks.

2021 ◽  
Vol 13 (20) ◽  
pp. 11340
Pedro Manuel Sousa ◽  
Maria João Moreira ◽  
Ana Pinto de de Moura ◽  
Rui Costa Lima ◽  
Luís Miguel Cunha

Every year, agri-food industries in industrialised countries produce approximately 1.3 billion tonnes of food loss and waste. The adoption of a circular economy policy has received special attention by the agri-food industries, allowing for the creation and development of new food products made of by-products that would otherwise be wasted or used for secondary applications. The present work, of an exploratory nature, aims to assess how consumers conceptualise the circular economy in order to identify consumer recognition of the use of by-products from the food industry to upcycle food products and to evaluate attitudes towards the circular economy. To this end, a mixed-methodology was applied to 340 participants. The first part was qualitative and used free word association to evaluate consumers’ conceptualisation of the circular economy and use of by-products as foods. Data were analysed by grouping the responses into exclusive and exhaustive categories and a correspondence analysis was also performed to originate perceptual maps. Additionally, a questionnaire was designed to evaluate major concepts and attitudes correlated with the circular economy. Data were reduced by principal component analysis (PCA) and participants grouped through clustering. Results showed that consumers understand circular economy as related mainly into Sustainability, Economy, and Circularity dimensions. Participants had great difficulty identifying the by-products used as foods or as food ingredients. From the quantitative data, four groups were identified based on the associations to the six principal components originated by the PCA. However, the results highlighted a very low association with all clusters of the Food Valorisation dimension within the concept of the circular economy, and also a lack of a clear understanding of consumers’ attitudes towards food products from the circular economy. Greater promotion and dissemination by the competent entities aimed at the general public may contribute towards greater integration, participation and acceptance of the circular economy concept for the upscaling of food by-products.

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