scholarly journals Multidimensionality evaluation of supply chain management integration

2018 ◽  
Vol 9 (1) ◽  
pp. 170
Author(s):  
Fernanda Paes Arantes ◽  
Maria Silene Alexandre Leite ◽  
Antonio Cezar Bornia ◽  
Pedro Alberto Barbetta
Keyword(s):  
Supply Chain ◽  
Essential Feature ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Response Options ◽  
Lack Of Information ◽  
Information Item ◽  
Item Factor Analysis ◽  
Dimensionality Analysis ◽  
Dichotomous Variables

Integration is constantly reported in the literature as an essential feature of SCM. However, it is as difficult to define as to operationalize it, resulting in a lack of information on how to increase the level of integration among members. Thus, this research aims to evaluate the dimensionality of SCI, to check alignment with the features identified in the literature using multivariate statistical analysis. The methodology used was based on a questionnaire to assess the level of companies integration with suppliers of its supply chain. This instrument consisted of 21 items, with response options: yes, no or not applicable. The questions focused on evaluating the integration with suppliers, so each company consulted could answer a questionnaire for each supplier, since the intensity of relationships varies. Thus, 41 companies participated in the survey, resulting in 205 responses. Full-information item factor analysis and principal component analysis on the correlation matrix tetrachoric were used for the instrument dimensionality analysis. They are appropriate procedures for analysis of dichotomous variables. The results of this analysis enabled grouping the questionnaire items in the following underlying factors: trust, information sharing, partnership, collaboration or cooperation, and coordination. The identification of such factors such as dimensions of SCI enables greater understanding on how to raise the level of integration among the members of a supply chain.

scholarly journals Habitat Preferences of PokeA (Batissa violacea var. celebensis von Martens, 1897) Basic on Substrat Characterisation in Pohara River Southeast Sulawesi

2020 ◽  
Vol 4 (2) ◽  
pp. 74-82
Author(s):  
Bahtiar Bahtiar ◽  
Muh. Fajar Purnama
Keyword(s):  
Habitat Preferences ◽  
Principal Component ◽  
Coarse Sand ◽  
Sediment Texture ◽  
Chi Square ◽  
Multivariate Statistical ◽  
Entire Cross Section ◽  
Lack Of Information ◽  
And Cluster Analysis ◽  
Sand And Gravel

This research is motivated by the lack of information about the habitat preferences of pokea clams in Pohara River, Southeast Sulawesi. This study aims to determine the density, distribution pattern and habitat preferences of pokea clams in the Pohara River, Southeast Sulawesi. This research was conducted for 6 months (April-September 2011). Sampling of pokea, water quality, and sediment texture was carried out in the Pohara River and analyzed at the FPIK UHO Laboratory. The density and distribution of pokea were calculated using a standard formula and analyzed using Mann Whitney and Chi Square respectively, while the habitat preferences based on different substrate textures were analyzed using Principal Component Analysis (PCA) and Cluster Analysis (CA) in the Multivariate Statistical Package (MVSP). The results showed that the density of pokea clams ranged from 117±96.78-816±594.84 ind/m2 which was distributed in cluster over the entire cross-section of the river. Pokea clams were found in all substrate textures from gravel to clay. The habitat preference of pokea clams indicated by the highest density was found in the clay texture. Pokea clams relatively do not like the habitat of coarse sand and gravel texture which is characterized by pokea population with the lowest density

Statistical Analsysis to Evaluate Heavy Metal Pollution in the Air Obatained by Moss Technique in Hanoi and its Surrounding Region

2019 ◽  
Vol 29 (3SI) ◽  
pp. 411
Author(s):  
N. H. Quyet ◽  
Le Hong Khiem ◽  
V. D. Quan ◽  
T. T. T. My ◽  
M. V. Frontasieva ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Statistical Analysis ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Five Factors ◽  
Heavy Metal Elements ◽  
Surrounding Areas ◽  
Potential Pollution

The aim of this paper was the application of statistical analysis including principal component analysis to evaluate heavy metal pollution obtained by moss technique in the air of Ha Noi and its surrounding areas and to evaluate potential pollution sources. The concentrations of 33 heavy metal elements in 27 samples of Barbula Indica moss in the investigated region collected in December of 2016 in the investigated area have been examined using multivariate statistical analysis. Five factors explaining 80% of the total variance were identified and their potential sources have been discussed.

It is better an approximate answer to the right question than the exact answer to the wrong question : the case of the psychometric analysis of the ASQ:SE

2020 ◽  
Author(s):  
Luis Anunciacao ◽  
janet squires ◽  
J. Landeira-Fernandez
Keyword(s):  
Internal Structure ◽  
Statistical Methods ◽  
Principal Component ◽  
Psychological Theory ◽  
Published Data ◽  
Multivariate Statistical ◽  
Exact Answer ◽  
Wide Range ◽  
Ages And Stages Questionnaire ◽  
The Right

One of the main activities in psychometrics is to analyze the internal structure of a test. Multivariate statistical methods, including Exploratory Factor analysis (EFA) and Principal Component Analysis (PCA) are frequently used to do this, but the growth of Network Analysis (NA) places this method as a promising candidate. The results obtained by these methods are of valuable interest, as they not only produce evidence to explore if the test is measuring its intended construct, but also to deal with the substantive theory that motivated the test development. However, these different statistical methods come up with different answers, providing the basis for different analytical and theoretical strategies when one needs to choose a solution. In this study, we took advantage of a large volume of published data (n = 22,331) obtained by the Ages and Stages Questionnaire Social-Emotional (ASQ:SE), and formed a subset of 500 children to present and discuss alternative psychometric solutions to its internal structure, and also to its subjacent theory. The analyses were based on a polychoric matrix, the number of factors to retain followed several well-known rules of thumb, and a wide range of exploratory methods was fitted to the data, including EFA, PCA, and NA. The statistical outcomes were divergent, varying from 1 to 6 domains, allowing a flexible interpretation of the results. We argue that the use of statistical methods in the absence of a well-grounded psychological theory has limited applications, despite its appeal. All data and codes are available at https://osf.io/z6gwv/.

scholarly journals Characterization of the Volatile Profile of Cultivated and Wild-Type Italian Celery (Apium graveolens L.) Varieties by HS-SPME/GC-MS

2021 ◽  
Vol 11 (13) ◽  
pp. 5855
Author(s):  
Samantha Reale ◽  
Valter Di Cecco ◽  
Francesca Di Donato ◽  
Luciano Di Martino ◽  
Aurelio Manzi ◽  
...  
Keyword(s):  
Solid Phase ◽  
Principal Component ◽  
Apium Graveolens ◽  
Gas Chromatography Mass Spectrometry ◽  
Bioactive Metabolites ◽  
Chemical Components ◽  
Volatile Profile ◽  
Wild Type ◽  
Multivariate Statistical ◽  
Clear Differentiation

Celery (Apium graveolens L.) is a vegetable belonging to the Apiaceae family that is widely used for its distinct flavor and contains a variety of bioactive metabolites with healthy properties. Some celery ecotypes cultivated in specific territories of Italy have recently attracted the attention of consumers and scientists because of their peculiar sensorial and nutritional properties. In this work, the volatile profiles of white celery “Sedano Bianco di Sperlonga” Protected Geographical Indication (PGI) ecotype, black celery “Sedano Nero di Torricella Peligna” and wild-type celery were investigated using head-space solid-phase microextraction combined with gas-chromatography/mass spectrometry (HS-SPME/GC-MS) and compared to that of the common ribbed celery. Exploratory multivariate statistical analyses were conducted using principal component analysis (PCA) on HS-SPME/GC-MS patterns, separately collected from celery leaves and petioles, to assess similarity/dissimilarity in the flavor composition of the investigated varieties. PCA revealed a clear differentiation of wild-type celery from the cultivated varieties. Among the cultivated varieties, black celery “Sedano Nero di Torricella Peligna” exhibited a significantly different composition in volatile profile in both leaves and petioles compared to the white celery and the prevalent commercial variety. The chemical components of aroma, potentially useful for the classification of celery according to the variety/origin, were identified.

scholarly journals Chemical Fingerprinting of Cryptic Species and Genetic Lineages of Aneura pinguis (L.) Dumort. (Marchantiophyta, Metzgeriidae)

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1180
Author(s):  
Rafał Wawrzyniak ◽  
Wiesław Wasiak ◽  
Beata Jasiewicz ◽  
Alina Bączkiewicz ◽  
Katarzyna Buczkowska
Keyword(s):  
Cluster Analysis ◽  
Cryptic Species ◽  
Plant Material ◽  
Chemical Constituents ◽  
Principal Component ◽  
Molecular Data ◽  
Rapid Identification ◽  
Multivariate Statistical ◽  
Genetic Lineages ◽  
Marker Compounds

Aneura pinguis (L.) Dumort. is a representative of the simple thalloid liverworts, one of the three main types of liverwort gametophytes. According to classical taxonomy, A. pinguis represents one morphologically variable species; however, genetic data reveal that this species is a complex consisting of 10 cryptic species (named by letters from A to J), of which four are further subdivided into two or three evolutionary lineages. The objective of this work was to develop an efficient method for the characterisation of plant material using marker compounds. The volatile chemical constituents of cryptic species within the liverwort A. pinguis were analysed by GC-MS. The compounds were isolated from plant material using the HS-SPME technique. Of the 66 compounds examined, 40 were identified. Of these 40 compounds, nine were selected for use as marker compounds of individual cryptic species of A. pinguis. A guide was then developed that clarified how these markers could be used for the rapid identification of the genetic lineages of A. pinguis. Multivariate statistical analyses (principal component and cluster analysis) revealed that the chemical compounds in A. pinguis made it possible to distinguish individual cryptic species (including genetic lineages), with the exception of cryptic species G and H. The classification of samples based on the volatile compounds by cluster analysis reflected phylogenetic relationships between cryptic species and genetic lineages of A. pinguis revealed based on molecular data.

scholarly journals Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films

2021 ◽  
Vol 11 (13) ◽  
pp. 5895
Author(s):  
Kristina Serec ◽  
Sanja Dolanski Babić
Keyword(s):  
Thin Films ◽  
Learning Algorithm ◽  
Principal Component Regression ◽  
Principal Component ◽  
Conformational Transitions ◽  
Cancer Diagnostics ◽  
Dna Conformation ◽  
Support Vector ◽  
Multivariate Statistical ◽  
The Impact

The double-stranded B-form and A-form have long been considered the two most important native forms of DNA, each with its own distinct biological roles and hence the focus of many areas of study, from cellular functions to cancer diagnostics and drug treatment. Due to the heterogeneity and sensitivity of the secondary structure of DNA, there is a need for tools capable of a rapid and reliable quantification of DNA conformation in diverse environments. In this work, the second paper in the series that addresses conformational transitions in DNA thin films utilizing FTIR spectroscopy, we exploit popular chemometric methods: the principal component analysis (PCA), support vector machine (SVM) learning algorithm, and principal component regression (PCR), in order to quantify and categorize DNA conformation in thin films of different hydrated states. By complementing FTIR technique with multivariate statistical methods, we demonstrate the ability of our sample preparation and automated spectral analysis protocol to rapidly and efficiently determine conformation in DNA thin films based on the vibrational signatures in the 1800–935 cm−1 range. Furthermore, we assess the impact of small hydration-related changes in FTIR spectra on automated DNA conformation detection and how to avoid discrepancies by careful sampling.

scholarly journals STATISTICAL MULTIVARIATE ANALYSIS APPLIED TO ENVIRONMENTAL CHARACTERIZATION OF SOIL IN SEMIARID REGION1

2019 ◽  
Vol 32 (1) ◽  
pp. 200-210
Author(s):  
Antônio Italcy de Oliveira Júnior ◽  
Luiz Alberto Ribeiro Mendonça ◽  
Sávio de Brito Fontenele ◽  
Adriana Oliveira Araújo ◽  
Maria Gorethe de Sousa Lima Brito
Keyword(s):  
Organic Matter Content ◽  
Principal Component ◽  
Matter Content ◽  
Multivariate Statistical ◽  
Statistical Tool ◽  
Environmental Characterization ◽  
Hierarchical Grouping ◽  
Two Factors ◽  
Total Data

ABSTRACT Soil is a dynamic and complex system that requires a considerable number of samples for analysis and research purposes. Using multivariate statistical methods, favorable conditions can be created by analyzing the samples, i.e., structural reduction and simplification of the data. The objective of this study was to use multivariate statistical analysis, including factorial analysis (FA) and hierarchical groupings, for the environmental characterization of soils in semiarid regions, considering anthropic (land use and occupation) and topographic aspects (altitude, moisture, granulometry, PR, and organic-matter content). As a case study, the São José Hydrographic Microbasin, which is located in the Cariri region of Ceará, was considered. An FA was performed using the principal component method, with normalized varimax rotation. In hierarchical grouping analysis, the “farthest neighbor” method was used as the hierarchical criterion for grouping, with the measure of dissimilarity given by the “square Euclidean distance.” The FA indicated that two factors explain 75.76% of the total data variance. In the analysis of hierarchical groupings, the samples were agglomerated in three groups with similar characteristics: one with samples collected in an area of the preserved forest and two with samples collected in areas with more anthropized soils. This indicates that the statistical tool used showed sensitivity to distinguish the most conserved soils and soils with different levels of anthropization.

scholarly journals Continuum Power CCA: A Unified Approach for Isolating Coupled Modes

2015 ◽  
Vol 28 (3) ◽  
pp. 1016-1030 ◽  
Author(s):  
Erik Swenson
Keyword(s):  
Signal To Noise Ratio ◽  
Full Range ◽  
Synthetic Data ◽  
Principal Component Regression ◽  
Principal Component ◽  
Accurate Estimate ◽  
Unified Approach ◽  
Coupled Modes ◽  
Multivariate Statistical ◽  
Sample Covariance

Abstract Various multivariate statistical methods exist for analyzing covariance and isolating linear relationships between datasets. The most popular linear methods are based on singular value decomposition (SVD) and include canonical correlation analysis (CCA), maximum covariance analysis (MCA), and redundancy analysis (RDA). In this study, continuum power CCA (CPCCA) is introduced as one extension of continuum power regression for isolating pairs of coupled patterns whose temporal variation maximizes the squared covariance between partially whitened variables. Similar to the whitening transformation, the partial whitening transformation acts to decorrelate individual variables but only to a partial degree with the added benefit of preconditioning sample covariance matrices prior to inversion, providing a more accurate estimate of the population covariance. CPCCA is a unified approach in the sense that the full range of solutions bridges CCA, MCA, RDA, and principal component regression (PCR). Recommended CPCCA solutions include a regularization for CCA, a variance bias correction for MCA, and a regularization for RDA. Applied to synthetic data samples, such solutions yield relatively higher skill in isolating known coupled modes embedded in noise. Provided with some crude prior expectation of the signal-to-noise ratio, the use of asymmetric CPCCA solutions may be justifiable and beneficial. An objective parameter choice is offered for regularization with CPCCA based on the covariance estimate of O. Ledoit and M. Wolf, and the results are quite robust. CPCCA is encouraged for a range of applications.

scholarly journals Penerapan Kansei Engineering dalam Perbandingan Desain Aplikasi Mobile Marketplace di Indonesia

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Nucky Vilano ◽  
Setia Budi
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Mobile Application ◽  
Principal Component ◽  
Kansei Engineering ◽  
Multivariate Statistical ◽  
Christian University ◽  
Emotional Factors ◽  
Coefficient Corrélation ◽  
Major Factors

The company's application design is very important because it displays the company's image and to attract more users to purchase/utilize the application. This research applies Kansei Engineering Method to analyze the emotion or feelings of the user towards the design of a mobile application interface. Six Kansei Words and three specimens are utilised in this research, where Kansei words are selected from words related to user experience. The participants of this research consist of 54 students from Maranatha Christian University. Participants’ responses are studied using multivariate statistical analysis (e.g., Coefficient Correlation Analysis, Principal Component Analysis, and Factor Analysis). This study explores the emotional factors that occur in designing an application. This analysis shows that there are some major factors that greatly influence the design of a mobile application interface.

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