A proposed winter-injury classification for apple trees on the northern fringe of commercial production

1992 ◽  
Vol 72 (2) ◽  
pp. 507-516 ◽  
Author(s):  
Warren K. Coleman
Keyword(s):  
Cluster Analysis ◽  
New Brunswick ◽  
Air Temperature ◽  
Hierarchical Classification ◽  
Principal Component ◽  
Fruit Production ◽  
Agglomerative Clustering ◽  
Apple Trees ◽  
Hierarchical Agglomerative Clustering ◽  
Temperature Records

Because of a severe and highly variable winter environment, commercially significant fruit production in New Brunswick is restricted to a few hardy apple cultivars. Cluster, principal-component and discriminant analyses were applied to monthly temperature records to derive a satisfactory classification of recorded occurrences of winter injury in New Brunswick during the 20th century. Production of a dendrogram based on a hierarchical, agglomerative clustering technique separated root- from shoot-injury years. The analyses suggested that low temperatures per se in December, January or February are not the dominant factors controlling recurring winter shoot injury of apple trees in New Brunswick. Rather mild weather during mid-winter (especially maximum air temperature in February) and the October mean air temperature during the fall hardening-off period consistently contributed to the hierarchical classification. Cluster analysis allowed the separation of recorded occurrences of winter injury into plausible groupings that should complement current attempts to understand the underlying causes of winter injury in New Brunswick.Key words: Malus × domestica, apple, winter injury, cluster analysis

scholarly journals Chromatographic, Chemometric and Antioxidant Assessment of the Equivalence of Granules and Herbal Materials of Angelicae Sinensis Radix

Medicines ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. 35
Author(s):  
Valentina Razmovski-Naumovski ◽  
Xian Zhou ◽  
Ho Yee Wong ◽  
Antony Kam ◽  
Jarryd Pearson ◽  
...  
Keyword(s):  
Ferulic Acid ◽  
Caffeic Acid ◽  
Radical Scavenging ◽  
Principal Component ◽  
Ultra Performance Liquid Chromatography ◽  
Array Detector ◽  
Agglomerative Clustering ◽  
Antioxidant Power ◽  
Hierarchical Agglomerative Clustering ◽  
Angelicae Sinensis

Background: Granules are a popular way of administrating herbal decoctions. However, there are no standardised quality control methods for granules, with few studies comparing the granules to traditional herbal decoctions. This study developed a multi-analytical platform to compare the quality of granule products to herb/decoction pieces of Angelicae Sinensis Radix (Danggui). Methods: A validated ultra-performance liquid chromatography coupled with photodiode array detector (UPLC-PDA) method quantitatively compared the aqueous extracts. Hierarchical agglomerative clustering analysis (HCA) and principal component analysis (PCA) clustered the samples according to three chemical compounds: ferulic acid, caffeic acid and Z-ligustilide. Ferric ion-reducing antioxidant power (FRAP) and 2,2-Diphenyl-1-picrylhydrazyl radical scavenging capacity (DPPH) assessed the antioxidant activity of the samples. Results: HCA and PCA allocated the samples into two main groups: granule products and herb/decoction pieces. Greater differentiation between the samples was obtained with three chemical markers compared to using one marker. The herb/decoction pieces group showed comparatively higher extraction yields and significantly higher DPPH and FRAP (p < 0.05), which was positively correlated to caffeic acid and ferulic acid, respectively. Conclusions: The results confirm the need for the quality assessment of granule products using more than one chemical marker for widespread practitioner and consumer use.

scholarly journals Ecohydrological threats to Colophospermum mopane in southern Zimbabwe

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
Tendayi Gondo ◽  
Agnes Musyoki ◽  
Aina T. Adeboyejo
Keyword(s):  
Management Practices ◽  
Life Support ◽  
Rural Households ◽  
Large Population ◽  
Principal Component ◽  
Agglomerative Clustering ◽  
Colophospermum Mopane ◽  
Hierarchical Agglomerative Clustering ◽  
Arid Landscapes ◽  
Semi Arid

Rapid ecohydrological changes in semi-arid landscapes are increasingly threatening humanity’s life-support systems and eroding many of the ecosystem services (ESs) upon which humans occupying such regions depend. Knowing which services and ecohydrological changes to be most concerned about is indispensable to maintaining the general health of such ecosystems and for developing effective ecosystem management practices. In the semi-arid regions of southwestern Zimbabwe where a large population of rural households depend on ESs extracted from the Colophospermum mopane tree, such understanding may be critical in reversing potential ES losses that may have catastrophic effects on the lives of many. We surveyed a total of 127 rural households who occupy the semi-arid landscapes of the Colophospermum mopane belt in southern Zimbabwe. We assessed the ecohydrological conditions characterising ecosystems where they obtain ES provisioning goods using a number of ecohydrological variables commonly cited in the literature on ecohydrology. Building on principal component analysis (PCA), we employed a hierarchical agglomerative clustering method to create unique clusters of households that depicted different levels of risks or threats associated with their ES provisioning harvesting practices. Multiple regression analysis was further performed to identify significant ecohydrological cluster-defining variables. Our results showed that spatial differences in ecohydrological parameters resulted in four distinct ES resource thresholds depicting four categories of risks that households face in extracting such resources in nearby landscapes. We concluded by proposing a number of landscape restoration or management practices targeted at reversing potential ES losses and subsequently safeguarding the livelihoods of many who depend on ESs.

scholarly journals Assessment by Multivariate Analysis of Groundwater between Low and High Tides Interactions in East Coast of Terengganu

2018 ◽  
Vol 7 (3.14) ◽  
pp. 80
Author(s):  
Hafizan Juahir ◽  
Muhammad Barzani Gasim ◽  
Mohd. Khairul Amri Kamarudin ◽  
Azman Azid ◽  
Norsyuhada Hairoma ◽  
...  
Keyword(s):  
High Tide ◽  
Principal Component ◽  
Agglomerative Clustering ◽  
Physico Chemical ◽  
Groundwater Samples ◽  
Positive Loading ◽  
Hierarchical Agglomerative Clustering ◽  
Compositional Pattern ◽  
Dry Seasons ◽  
The Difference

World sea level rise has an effect in the rise on high and low tides levels in coastal areas of Terengganu. Because of that, as many as 13 groundwater represented of well that located close to Terengganu coastline were sampled and analyzed. Samplings were conducted for the wet and dry seasons and also for the high and low tides at the same sampling wells to identify the variation of groundwater quality temporally. A Global Positioning System (GPS) was used to locate the exact coordinates of each sampling well. Nineteen physico-chemical parameters were analyzed from groundwater samples. Principal Component Analysis (PCA) was adopted to observe the contrast of the compositional pattern among the variables and to recognize the factors that influence the parameters as an input to define water intrusion. Hierarchical Agglomerative Clustering Analysis (HACA) is performed on data to group the sampling wells into a few clusters. The results show that from nineteen parameters only five has strong positive loading; EC (0.99), TDS (0.99), chloride (0.99), sulphate (0.92) and salinity (0.99) during high and low tides. The difference are BOD and DO have strong positive loading during low tide while turbidity and TSS were strong positive loading during high tide.  

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Quality evaluation of tugaksheeree samples by ATR-FTIR spectroscopy using multicomponent analysis

2020 ◽  
Vol 10 ◽  
Author(s):  
Nikunj D. Patel ◽  
Niranjan S. Kanaki
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Quality Evaluation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Attenuated Total Reflectance ◽  
Chemometric Methods ◽  
Total Reflectance ◽  
Fingerprint Region ◽  
Ftir Technique

Background: Numerous Ayurvedic formulations contains tugaksheeree as key ingredient. Tugaksheereeis the starch gained from the rhizomes of two plants, Curcuma angustifoliaRoxb. (Zingiberaceae) and Marantaarundinacea (MA) Linn. (Marantaceae). Objective: The primary concerns in quality assessment of Tugaksheeree occur due to adulteration or substitution. Method: In current study, Fourier transform infrared (FTIR) technique with attenuated total reflectance (ATR) facility was used to evaluate tugaksheeree samples. Total 10 different samples were studied and transmittance mode was kept to record the spectra devoid of pellets of KBR. Further treatment was given with multi component tools by considering fingerprint region of the spectra. Multivariate analysis was performed by various chemometric methods. Result: Multi component methods like Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA)were used to discriminate the tugaksheeree samples using Minitab software. Conclusion: This method can be used as a tool to differentiate samples of tugaksheeree from its adulterants and substitutes.

scholarly journals COVID-19 Pandemic and Adaptive Shopping Patterns: An Insight from Indonesian Consumers

2021 ◽  
pp. 097215092110135
Author(s):  
Arif Hartono ◽  
Asma'i Ishak ◽  
Agus Abdurrahman ◽  
Budi Astuti ◽  
Endy Gunanto Marsasi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Demographic Characteristics ◽  
Depth Information ◽  
Factor Scores ◽  
Consumer Spending ◽  
Consumer Responses ◽  
Buying Behaviour ◽  
Manova Analysis

Although existing studies on consumers typology are extensively conducted, insights on consumers typology in adapting their shopping attitude and behaviour during the COVID-19 pandemic remain unexplored. Current studies on consumer responses to the COVID-19 pandemic tend to focus on the following themes: panic buying behaviour, consumer spending and consumer consumption. This study explores a typology of adaptive shopping patterns in response to the COVID-19 pandemic. The study involved a survey of 465 Indonesian consumers. Principal component analysis is used to identify the variables related to adaptive shopping patterns. Cluster analysis of the factor scores obtained on the adaptive shopping attitude and behaviour revealed the typology of Indonesian shoppers’ adaptive patterns. Multivariate Analysis of Variance (MANOVA) analysis is used to profile the identified clusters based on attitude, behaviour and demographic characteristics. Results revealed five adaptive shopping patterns with substantial differences among them. This study provides in-depth information about the profile of Indonesian shoppers’ adaptive patterns that would help retailers in understanding consumers and choosing their target group. The major contribution of this study is providing segmentation on shopping adaptive patterns in the context of the COVID-19 pandemic which presents interesting differences compared with previous studies. This study reveals new insights on shoppers’ adaptive attitude and behaviour as consumers coped with the pandemic.

scholarly journals Clustering Techniques for Secondary Substations Siting

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1028
Author(s):  
Silvia Corigliano ◽  
Federico Rosato ◽  
Carla Ortiz Dominguez ◽  
Marco Merlo
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Universal Access ◽  
Distribution Networks ◽  
Industrialized Countries ◽  
Agglomerative Clustering ◽  
Clustering Techniques ◽  
Hierarchical Agglomerative Clustering ◽  
Efficient Planning ◽  
Target Set

The scientific community is active in developing new models and methods to help reach the ambitious target set by UN SDGs7: universal access to electricity by 2030. Efficient planning of distribution networks is a complex and multivariate task, which is usually split into multiple subproblems to reduce the number of variables. The present work addresses the problem of optimal secondary substation siting, by means of different clustering techniques. In contrast with the majority of approaches found in the literature, which are devoted to the planning of MV grids in already electrified urban areas, this work focuses on greenfield planning in rural areas. K-means algorithm, hierarchical agglomerative clustering, and a method based on optimal weighted tree partitioning are adapted to the problem and run on two real case studies, with different population densities. The algorithms are compared in terms of different indicators useful to assess the feasibility of the solutions found. The algorithms have proven to be effective in addressing some of the crucial aspects of substations siting and to constitute relevant improvements to the classic K-means approach found in the literature. However, it is found that it is very challenging to conjugate an acceptable geographical span of the area served by a single substation with a substation power high enough to justify the installation when the load density is very low. In other words, well known standards adopted in industrialized countries do not fit with developing countries’ requirements.

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