Distribution, diversity and potential agronomic value of Medicago polymorpha in Sardinia

1995 ◽  
Vol 124 (3) ◽  
pp. 419-426 ◽  
Author(s):  
A. Loi ◽  
C. Porqueddu ◽  
F. Veronesi ◽  
P. S. Cocks
Keyword(s):  
Seed Production ◽  
Principal Component ◽  
Future Research ◽  
Medicago Polymorpha ◽  
Winter Temperatures ◽  
Single Entry ◽  
Morphological Variables ◽  
To Come ◽  
Cluster 2 ◽  
Better Than

SUMMARYThirty-five populations of Medicago polymorpha were collected from throughout Sardinia (Italy) in 1989 with a view to developing pasture species suitable for improving degraded grasslands in the northern Mediterranean basin. Herbage and seed production were compared with the Australian cultivar, Circle Valley, over 2 years at Bonassai, north Sardinia. Regeneration in the 2 years after sowing and seed size were also estimated. All variables were related to collection site parameters using multivariate analysis.Herbage production varied between 2 and 8 t dry matter/ha, and up to 1·5 t/ha of seed was produced. K-means clustering of agronomic and morphological variables indicated that there were two groups present; one similar to variety polymorpha and one to variety vulgaris. There were two other single entry clusters, one of which contained cv. Circle Valley. Principal component analysis of the environmental variables indicated that cluster 1 (vulgaris) was more likely to come from mountainous areas where winter temperatures are low, and cluster 2 (polymorpha) from coastal areas where temperatures are mild. Regeneration of cluster 1 was better than that of cluster 2, which in turn was better than Circle Valley, indicating that populations in cluster 1 are better adapted to the management system imposed at Bonassai.The results indicate that M. polymorpha has considerable potential to improve the grasslands of Sardinia. However, it is unlikely that imported cultivars will be successful, and it seems important that the selection of local populations should continue. Commercial seed production in Sardinia is likely to be a problem, and grazing management under the conditions of communal ownership may have to be reviewed. It is important that future research and development involves farmers and other industry groups.

scholarly journals Lipreading in School-Age Children: The Roles of Age, Hearing Status, and Cognitive Ability

2014 ◽  
Vol 57 (2) ◽  
pp. 556-565 ◽  
Author(s):  
Nancy Tye-Murray ◽  
Sandra Hale ◽  
Brent Spehar ◽  
Joel Myerson ◽  
Mitchell S. Sommers
Keyword(s):  
Working Memory ◽  
Hearing Loss ◽  
Time Course ◽  
Principal Component ◽  
Developmental Time ◽  
School Age Children ◽  
Future Research ◽  
Visuospatial Working Memory ◽  
Hearing Status ◽  
Better Than

Purpose The study addressed three research questions: Does lipreading improve between the ages of 7 and 14 years? Does hearing loss affect the development of lipreading? How do individual differences in lipreading relate to other abilities? Method Forty children with normal hearing (NH) and 24 with hearing loss (HL) were tested using 4 lipreading instruments plus measures of perceptual, cognitive, and linguistic abilities. Results For both groups, lipreading performance improved with age on all 4 measures of lipreading, with the HL group performing better than the NH group. Scores from the 4 measures loaded strongly on a single principal component. Only age, hearing status, and visuospatial working memory were significant predictors of lipreading performance. Conclusions Results showed that children's lipreading ability is not fixed but rather improves between 7 and 14 years of age. The finding that children with HL lipread better than those with NH suggests experience plays an important role in the development of this ability. In addition to age and hearing status, visuospatial working memory predicts lipreading performance in children, just as it does in adults. Future research on the developmental time-course of lipreading could permit interventions and pedagogies to be targeted at periods in which improvement is most likely to occur.

scholarly journals Genetic Diversity Analysis for Yield Associated and Quality Traits in Promising Rice Varieties of Tamil Nadu

2021 ◽  
Vol 12 (5) ◽  
pp. 361-369
Author(s):  
M. Vinod Kumar Naik ◽  
◽  
M. Arumugam Pillai ◽  
S. Saravanan ◽  
◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Tamil Nadu ◽  
Amylose Content ◽  
Principal Component ◽  
Rice Varieties ◽  
Single Entry ◽  
Cluster Distance ◽  
Maximum Cluster ◽  
Cluster 2

An experiment was conducted with 55 rice varieties to assess the genetic diversity by using Mahalanobis D2 Statistical and characterization of genotypes using principal component analysis. All genotypes exhibited a wide and significant variation for 19 traits, by cluster analysis grouped into ten clusters. The maximum genotypes were included in Cluster 6 (16) followed by cluster 4 (10), cluster 3 (8), cluster 2 (7), cluster 5 (5), cluster 8 (4), cluster 1 (2), with 29.09, 18.18, 14.54, 12.72, 9.09, 7.27 and 3.63 proportion respectively, the rest of three clusters had one genotype each. Maximum cluster distance obtained between cluster×constituted by single entry (Pusa Basmati) showed highest inter cluster distance from cluster V (20727.37), VII (18414.79), I (17228.89) and cluster III (17010.24) are having very high inter cluster distance and also by cluster IX from cluster VIII (8852.36), VI (7559.67), I (7444.68) and cluster VII (6666.83) followed by cluster VI from cluster V (6225.95). The lowest inter cluster distance was observed between cluster II and cluster IV III and VI followed by between cluster I and cluster VIII, XI, II, VI and cluster IV. The intra cluster D2 values ranged from Zero (Cluster VII, IX, X) to 2233.91 (Cluster VIII). Contribution of amylose content was highest towards genetic divergence (23.43%) by taking 348 times ranked first followed by days to 50% flowering (23.37%) by 347 times, single plant yield (23.3%) by 346 times. The PCA analysis showed that first eight principal components accounted for about 85.4%.

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

scholarly journals SPPD: A Novel Reassembly Method for 3D Terracotta Warrior Fragments Based on Fracture Surface Information

2021 ◽  
Vol 10 (8) ◽  
pp. 525
Author(s):  
Wenmin Yao ◽  
Tong Chu ◽  
Wenlong Tang ◽  
Jingyu Wang ◽  
Xin Cao ◽  
...  
Keyword(s):  
Fracture Surface ◽  
Principal Component ◽  
Point Clouds ◽  
Three Dimension ◽  
Registration Method ◽  
Series Of Experiments ◽  
Coarse To Fine ◽  
Fine Registration ◽  
Common Situation ◽  
Better Than

As one of China′s most precious cultural relics, the excavation and protection of the Terracotta Warriors pose significant challenges to archaeologists. A fairly common situation in the excavation is that the Terracotta Warriors are mostly found in the form of fragments, and manual reassembly among numerous fragments is laborious and time-consuming. This work presents a fracture-surface-based reassembling method, which is composed of SiamesePointNet, principal component analysis (PCA), and deep closest point (DCP), and is named SPPD. Firstly, SiamesePointNet is proposed to determine whether a pair of point clouds of 3D Terracotta Warrior fragments can be reassembled. Then, a coarse-to-fine registration method based on PCA and DCP is proposed to register the two fragments into a reassembled one. The above two steps iterate until the termination condition is met. A series of experiments on real-world examples are conducted, and the results demonstrate that the proposed method performs better than the conventional reassembling methods. We hope this work can provide a valuable tool for the virtual restoration of three-dimension cultural heritage artifacts.

scholarly journals Age, Sex and Cerebral Microbleed Effects On White Matter Degradation After Traumatic Brain Injury

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 886-887
Author(s):  
Andrei Irimia ◽  
Ammar Dharani ◽  
Van Ngo ◽  
David Robles ◽  
Kenneth Rostowsky
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
White Matter ◽  
Diffusion Tensor ◽  
Diffuse Axonal Injury ◽  
Principal Component ◽  
Older Age ◽  
Analysis Of Covariance ◽  
Future Research ◽  
Cerebral Microbleed

Abstract Mild traumatic brain injury (mTBI) affects white matter (WM) integrity and accelerates neurodegeneration. This study assesses the effects of age, sex, and cerebral microbleed (CMB) load as predictors of WM integrity in 70 subjects aged 18-77 imaged acutely and ~6 months after mTBI using diffusion tensor imaging (DTI). Two-tensor unscented Kalman tractography was used to segment and cluster 73 WM structures and to map changes in their mean fractional anisotropy (FA), a surrogate measure of WM integrity. Dimensionality reduction of mean FA feature vectors was implemented using principal component (PC) analysis, and two prominent PCs were used as responses in a multivariate analysis of covariance. Acutely and chronically, older age was significantly associated with lower FA (F2,65 = 8.7, p < .001, η2 = 0.2; F2,65 = 12.3, p < .001, η2 = 0.3, respectively), notably in the corpus callosum and in dorsolateral temporal structures, confirming older adults’ WM vulnerability to mTBI. Chronically, sex was associated with mean FA (F2,65 = 5.0, p = 0.01, η2 = 0.1), indicating males’ greater susceptibility to WM degradation. Acutely, a significant association was observed between CMB load and mean FA (F2,65 = 5.1, p = 0.009, η2 = 0.1), suggesting that CMBs reflect the acute severity of diffuse axonal injury. Together, these findings indicate that older age, male sex, and CMB load are risk factors for WM degeneration. Future research should examine how sex- and age-mediated WM degradation lead to cognitive decline and connectome degeneration after mTBI.

scholarly journals Variety Identification of Orchids Using Fourier Transform Infrared Spectroscopy Combined with Stacked Sparse Auto-Encoder

Molecules ◽  
2019 ◽  
Vol 24 (13) ◽  
pp. 2506 ◽  
Author(s):  
Yunfeng Chen ◽  
Yue Chen ◽  
Xuping Feng ◽  
Xufeng Yang ◽  
Jinnuo Zhang ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Spectroscopic Technique ◽  
Variety Identification ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Discriminant Models ◽  
The Fourier Transform ◽  
Better Than

The feasibility of using the fourier transform infrared (FTIR) spectroscopic technique with a stacked sparse auto-encoder (SSAE) to identify orchid varieties was studied. Spectral data of 13 orchids varieties covering the spectral range of 4000–550 cm−1 were acquired to establish discriminant models and to select optimal spectral variables. K nearest neighbors (KNN), support vector machine (SVM), and SSAE models were built using full spectra. The SSAE model performed better than the KNN and SVM models and obtained a classification accuracy 99.4% in the calibration set and 97.9% in the prediction set. Then, three algorithms, principal component analysis loading (PCA-loading), competitive adaptive reweighted sampling (CARS), and stacked sparse auto-encoder guided backward (SSAE-GB), were used to select 39, 300, and 38 optimal wavenumbers, respectively. The KNN and SVM models were built based on optimal wavenumbers. Most of the optimal wavenumbers-based models performed slightly better than the all wavenumbers-based models. The performance of the SSAE-GB was better than the other two from the perspective of the accuracy of the discriminant models and the number of optimal wavenumbers. The results of this study showed that the FTIR spectroscopic technique combined with the SSAE algorithm could be adopted in the identification of the orchid varieties.

The psychology quadrant: an outcome measure based on the START risk assessment

2017 ◽  
Vol 19 (1) ◽  
pp. 14-22
Author(s):  
Matthew John Gill ◽  
Samantha Brookes
Keyword(s):  
Risk Assessment ◽  
Factor Structure ◽  
Psychiatric Rehabilitation ◽  
Principal Component ◽  
Future Research ◽  
Psychological Outcome ◽  
Content Type ◽  
Health Rehabilitation ◽  
Mental Health Rehabilitation ◽  
Outcome Tool

Purpose The purpose of this paper is to develop a psychological outcome tool which reflects the relationship between clusters of items on the Short Term Assessment of Risk and Treatability (START) risk assessment and different categories of psychological progress in male inpatient psychiatric services. Design/methodology/approach A principal component analysis (PCA) was conducted on data from 135 male psychiatric rehabilitation patients’ START risk assessments. Findings PCA identified four strength psychology quadrants which were explained by a five-factor structure and four vulnerability quadrants which were explained by a four-factor structure. The development of the psychology quadrant, its usefulness in establishing a treatment pathway and areas of future research are also discussed. Originality/value Developing accessible, transparent outcome measures using evidence-based practice is highly relevant within the field of mental health rehabilitation.

Recent Reviews on Machine Vision-Based 3D Reconstruction

2021 ◽  
Vol 14 ◽  
Author(s):  
Hongxin Zhang ◽  
Shaowei Ma ◽  
Meng Li ◽  
Hanghang Jiang ◽  
Jiaming Li
Keyword(s):  
Machine Vision ◽  
3D Reconstruction ◽  
Autonomous Navigation ◽  
Robot Vision ◽  
Research Direction ◽  
Development Trend ◽  
Future Research ◽  
The Future ◽  
The Development Trend ◽  
Better Than

Background: In machine vision, the 3D reconstruction is widely used in medical system, autonomous navigation, aviation and remote sensing measurement, industrial automation and other fields, and the demand for reconstruction precision is significantly highlighted. Therefore, the 3D reconstruction is of great research value and will be an important research direction in the future. Objective: By reviewing the latest development and patent of 3D reconstruction, this paper provides references to researchers in related fields. Methods: Machine vision-based 3D reconstruction patents and literatures were analyzed from the aspects of the algorithm, innovation and application. Among them, there are more than 30 patents and nearly 30 literatures in the past ten years. Results: Researches on machine vision-based 3D reconstruction in recent 10 years are reviewed, and the typical characteristics were concluded. The main problems in its development were analyzed, the development trend was foreseen, and the current and future research on the productions and patents related to machine vision-based 3D reconstruction is discussed. Conclusion: The reconstruction result of binocular vision and multi-vision is better than monocular vision in most cases. Current researches of 3D reconstruction focus on robot vision navigation, intelligent vehicle environment sensing system and virtual reality. The aspects that need to be improved in the future include: improving robustness, reducing computational complexity, and reducing operating equipment requirements, and so on. Furthermore, more patents on machine vision-based 3D reconstruction also should be invented.

scholarly journals An Empirical Investigation on a Multiple Filters-Based Approach for Remaining Useful Life Prediction

Machines ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 35 ◽  
Author(s):  
Hung-Cuong Trinh ◽  
Yung-Keun Kwon
Keyword(s):  
Scoring Function ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Filter Method ◽  
Feature Construction ◽  
Training Set ◽  
Robust Solution ◽  
Benchmark Datasets ◽  
Useful Life ◽  
Better Than

Feature construction is critical in data-driven remaining useful life (RUL) prediction of machinery systems, and most previous studies have attempted to find a best single-filter method. However, there is no best single filter that is appropriate for all machinery systems. In this work, we devise a straightforward but efficient approach for RUL prediction by combining multiple filters and then reducing the dimension through principal component analysis. We apply multilayer perceptron and random forest methods to learn the underlying model. We compare our approach with traditional single-filtering approaches using two benchmark datasets. The former approach is significantly better than the latter in terms of a scoring function with a penalty for late prediction. In particular, we note that selecting a best single filter over the training set is not efficient because of overfitting. Taken together, we validate that our multiple filters-based approach can be a robust solution for RUL prediction of various machinery systems.

Estimating Store Brand Shelf Space: A New Framework Using Neural Networks and Partial Least Squares

2009 ◽  
Vol 51 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Monica Gomez ◽  
Shintaro Okazaki
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Statistical Treatment ◽  
Principal Component ◽  
Future Research ◽  
Store Brand ◽  
Shelf Space ◽  
Managerial Implications ◽  
Space Data

Despite abundant research that examines the effects of store brands on retail decision making, little attention has been paid to the predictive model of store brand shelf space. This paper intends to fill this research gap by proposing and testing a theoretical model of store brand shelf space. From the literature review, 11 independent variables were identified (i.e. store format, reputation, brand assortment, depth of assortment, in-store promotions, leading national brands’ rivalry, retailers’ rivalry, manufacturers’ concentration, store brand market share, advertising, and innovation) and analysed as potential predictors of the dependent variable (i.e. store brand shelf space). Data were collected for 29 product categories in 55 retail stores. In designing the statistical treatment, a three-phase procedure was adopted: (1) interdependence analysis via principal component analysis; (2) dependence analysis via neural network simulation; and (3) structural equation modelling via partial least squares. The findings corroborate our proposed model, in that all hypothesised relationships and directions are supported. On this basis, we draw theoretical as well as managerial implications. In closing, we acknowledge the limitations of this study and suggest future research directions.

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