Comparison of optimization methods for core subset selection from a large collection of Mexican wheat landraces characterized by SNP markers

2017 ◽  
Vol 16 (3) ◽  
pp. 228-236
Author(s):  
Carlos L. Acuña-Matamoros ◽  
M. Humberto Reyes-Valdés
Keyword(s):  
Subset Selection ◽  
Optimization Methods ◽  
Snp Markers ◽  
Replica Exchange ◽  
Objective Functions ◽  
Forward Selection ◽  
Data Set ◽  
Core Subset ◽  
Significant Difference ◽  
Wheat Landraces

AbstractCore subset selection from collections hosted by seed banks, grow in importance as the number of accessions and genetic marker information rapidly increases. A data set of 20,526 single-nucleotide polymorphism (SNP) markers characterizing 7986 Mexican creole wheat landraces, was used to test 11 methods for core subset selection, through optimization criteria containing average genetic distance and genetic diversity. Allele richness was used as an additional criterion to qualify the generated core subsets. Three replications with random samples of 1500 SNP loci, each comprising a maximum of 3000 alleles, were used to perform the method evaluations through four different objective functions. The LR greedy search (LR) and LR with random first pair (LRSemi) were consistently best across all assays for maximizing the objective functions, and they performed well even for criteria not included in those functions. The Tukey's HSD (honest significant difference) multiple comparisons grouped those methods together with the sequential forward selection (SFS) and SFS with random first pair (SFSSemi) strategies as the top set of approaches. All of them are simple heuristic maximization algorithms, and outperformed two more sophisticated optimization approaches: parallel mixed replica exchange and replica exchange Monte Carlo. For their efficiency to optimize the objective functions and computing speed, the LRSemi and SFSSemi methods demonstrated to be good alternatives for core subset selection from large collections of highly homozygous accessions characterized by many biallelic markers.

scholarly journals Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1106
Author(s):  
S. Bhaskaran ◽  
Raja Marappan ◽  
B. Santhi
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Optimization Methods ◽  
Tuning Parameter ◽  
Computational Time ◽  
Data Set ◽  
Significant Difference ◽  
Minimum Number ◽  
Tremendous Amount ◽  
The Given

Nowadays, because of the tremendous amount of information that humans and machines produce every day, it has become increasingly hard to choose the more relevant content across a broad range of choices. This research focuses on the design of two different intelligent optimization methods using Artificial Intelligence and Machine Learning for real-life applications that are used to improve the process of generation of recommenders. In the first method, the modified cluster based intelligent collaborative filtering is applied with the sequential clustering that operates on the values of dataset, user′s neighborhood set, and the size of the recommendation list. This strategy splits the given data set into different subsets or clusters and the recommendation list is extracted from each group for constructing the better recommendation list. In the second method, the specific features-based customized recommender that works in the training and recommendation steps by applying the split and conquer strategy on the problem datasets, which are clustered into a minimum number of clusters and the better recommendation list, is created among all the clusters. This strategy automatically tunes the tuning parameter λ that serves the role of supervised learning in generating the better recommendation list for the large datasets. The quality of the proposed recommenders for some of the large scale datasets is improved compared to some of the well-known existing methods. The proposed methods work well when λ = 0.5 with the size of the recommendation list, |L| = 30 and the size of the neighborhood, |S| < 30. For a large value of |S|, the significant difference of the root mean square error becomes smaller in the proposed methods. For large scale datasets, simulation of the proposed methods when varying the user sizes and when the user size exceeds 500, the experimental results show that better values of the metrics are obtained and the proposed method 2 performs better than proposed method 1. The significant differences are obtained in these methods because the structure of computation of the methods depends on the number of user attributes, λ, the number of bipartite graph edges, and |L|. The better values of the (Precision, Recall) metrics obtained with size as 3000 for the large scale Book-Crossing dataset in the proposed methods are (0.0004, 0.0042) and (0.0004, 0.0046) respectively. The average computational time of the proposed methods takes <10 seconds for the large scale datasets and yields better performance compared to the well-known existing methods.

scholarly journals Development of Ground Special Vehicle PHM with Case-Based Reason Model

2021 ◽  
Vol 11 (10) ◽  
pp. 4494
Author(s):  
Qicai Wu ◽  
Haiwen Yuan ◽  
Haibin Yuan
Keyword(s):  
Cross Validation ◽  
Optimization Methods ◽  
Engineering Practice ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Data Set ◽  
Attribute Weights ◽  
Special Vehicle ◽  
Weight Allocation ◽  
Case Based

The case-based reasoning (CBR) method can effectively predict the future health condition of the system based on past and present operating data records, so it can be applied to the prognostic and health management (PHM) framework, which is a type of data-driven problem-solving. The establishment of a CBR model for practical application of the Ground Special Vehicle (GSV) PHM framework is in great demand. Since many CBR algorithms are too complicated in weight optimization methods, and are difficult to establish effective knowledge and reasoning models for engineering practice, an application development using a CBR model that includes case representation, case retrieval, case reuse, and simulated annealing algorithm is introduced in this paper. The purpose is to solve the problem of normal/abnormal determination and the degree of health performance prediction. Based on the proposed CBR model, optimization methods for attribute weights are described. State classification accuracy rate and root mean square error are adopted to setup objective functions. According to the reasoning steps, attribute weights are trained and put into case retrieval; after that, different rules of case reuse are established for these two kinds of problems. To validate the model performance of the application, a cross-validation test is carried on a historical data set. Comparative analysis of even weight allocation CBR (EW-CBR) method, correlation coefficient weight allocation CBR (CW-CBR) method, and SA weight allocation CBR (SA-CBR) method is carried out. Cross-validation results show that the proposed method can reach better results compared with the EW-CBR model and CW-CBR model. The developed PHM framework is applied to practical usage for over three years, and the proposed CBR model is an effective approach toward the best PHM framework solutions in practical applications.

scholarly journals Ownership Variation in Violated Regulations and National Care Standards: Evidence From Social Care Providers

2021 ◽  
pp. 089976402110014
Author(s):  
Anders M. Bach-Mortensen ◽  
Ani Movsisyan
Keyword(s):  
Social Care ◽  
Negative Binomial ◽  
Third Sector ◽  
Care Providers ◽  
Data Set ◽  
Logistic Regression Models ◽  
For Profit ◽  
Care Services ◽  
Significant Difference ◽  
Research Gap

Social care services are increasingly provisioned in quasi-markets in which for-profit, public, and third sector providers compete for contracts. Existing research has investigated the implications of this development by analyzing ownership variation in latent outcomes such as quality, but little is known about whether ownership predicts variation in more concrete outcomes, such as violation types. To address this research gap, we coded publicly available inspection reports of social care providers regulated by the Care Inspectorate in Scotland and created a novel data set enabling analysis of ownership variation in violations of (a) regulations, and (b) national care standards over an entire inspection year ( n = 4,178). Using negative binomial and logistic regression models, we find that for-profit providers are more likely to violate non-enforceable outcomes (national care standards) relative to other ownership types. We did not identify a statistically significant difference between for-profit and third sector providers with regard to enforceable outcomes (regulations).

scholarly journals Adverse reactions of targeted therapy in cancer patients: a retrospective study of hospital medical data in China

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ruofei Du ◽  
Xin Wang ◽  
Lixia Ma ◽  
Leon M. Larcher ◽  
Han Tang ◽  
...  
Keyword(s):  
Targeted Therapy ◽  
Cancer Patients ◽  
Adverse Reactions ◽  
Cox Regression ◽  
Target Therapy ◽  
Skin Damage ◽  
Data Set ◽  
Patients With Cancer ◽  
Number Of Patients ◽  
Significant Difference

Abstract Background The adverse reactions (ADRs) of targeted therapy were closely associated with treatment response, clinical outcome, quality of life (QoL) of patients with cancer. However, few studies presented the correlation between ADRs of targeted therapy and treatment effects among cancer patients. This study was to explore the characteristics of ADRs with targeted therapy and the prognosis of cancer patients based on the clinical data. Methods A retrospective secondary data analysis was conducted within an ADR data set including 2703 patients with targeted therapy from three Henan medical centers of China between January 2018 and December 2019. The significance was evaluated with chi-square test between groups with or without ADRs. Univariate and multivariate logistic regression with backward stepwise method were applied to assess the difference of pathological characteristics in patients with cancer. Using the univariate Cox regression method, the actuarial probability of overall survival was performed to compare the clinical outcomes between these two groups. Results A total of 485 patients were enrolled in this study. Of all patients, 61.0% (n = 296) occurred ADRs including skin damage, fatigue, mucosal damage, hypertension and gastrointestinal discomfort as the top 5 complications during the target therapy. And 62.1% of ADRs were mild to moderate, more than half of the ADRs occurred within one month, 68.6% ADRs lasted more than one month. Older patients (P = 0.022) and patients with lower education level (P = 0.036), more than 2 comorbidities (P = 0.021), longer medication time (P = 0.022), drug combination (P = 0.033) and intravenous administration (P = 0.019) were more likely to have ADRs. Those with ADRs were more likely to stop taking (P = 0.000), change (P = 0.000), adjust (P = 0.000), or not take the medicine on time (P = 0.000). The number of patients with recurrence (P = 0.000) and metastasis (P = 0.006) were statistically significant difference between ADRs and non-ADRs group. And the patients were significantly poor prognosis in ADRs groups compared with non-ADRs group. Conclusion The high incidence of ADRs would affect the treatment and prognosis of patients with cancer. We should pay more attention to these ADRs and develop effective management strategies.

scholarly journals EPID-22. CHARACTERIZATION OF RADIATION-INDUCED MENINGIOMAS IN A BRAIN TUMOR DATABASE

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii83-ii83
Author(s):  
Nilan Vaghjiani ◽  
Andrew Schwieder ◽  
Sravya Uppalapati ◽  
Zachary Kons ◽  
Elizabeth Kazarian ◽  
...  
Keyword(s):  
African American ◽  
Brain Tumor ◽  
Radiation Treatment ◽  
Latency Period ◽  
Data Set ◽  
Who Grade ◽  
Significant Difference ◽  
Radiation Induced ◽  
Grade Ii ◽  
The Mean

Abstract PURPOSE Radiation-induced meningiomas (RIMs) are associated with previous exposure to therapeutic irradiation. RIMs are rare and have not been well characterized relative to spontaneous meningiomas (SMs). METHODS 1003 patients with proven or presumed meningiomas were identified from the VCU brain tumor database. Chart review classified RIM patients and their characteristics. RESULTS Of the 1003 total patients, 76.47% were female with a mean ± SD age of 67.55 ± 15.50 years. 15 RIM patients were identified (66.67% female), with a mean ± SD age of 52.67 ± 15.46 years, 5 were African American and 10 were Caucasian. The incidence of RIMs was 1.49% in our data set. The mean age at diagnosis was 43.27 ± 15.06 years. The mean latency was 356.27 ± 116.96 months. The mean initiating dose was 44.28 ± 14.68 Gy. There was a significant difference between mean latency period and ethnicity, 258.3 months for African American population, and 405.2 months for Caucasian population (p = 0.003). There was a significant difference between the mean number of lesions in females (2.8) versus males (1.2; p = 0.046). Of the RIMs with characterized histology, 6 (55%) were WHO grade II and 5 (45%) were WHO grade I, demonstrating a prevalence of grade II tumors approximately double that found with SMs. RIMs were treated with combinations of observation, surgery, radiation, and medical therapy. Of the 8 patients treated with radiation, 4 demonstrated response. 8 of the 15 patients (53%) demonstrated recurrence/progression despite treatment. CONCLUSION RIMs are important because of the associated higher grade histology, gender, and ethnic incidences, and increased recurrence/progression compared to SMs. Despite the presumed contributory role of prior radiation, RIMs demonstrate a significant rate of responsiveness to radiation treatment.

Detection of Ureteral Stones in Kidney Ureter Bladder Radiography: Usefulness of Digital Post-processing

2021 ◽  
Vol 17 ◽  
Author(s):  
Sang Lim Choi ◽  
Sung Bin Park ◽  
Seungwook Yang ◽  
Eun Sun Lee ◽  
Hyun Jeong Park ◽  
...  
Keyword(s):  
Detection Sensitivity ◽  
Ureteral Stones ◽  
Data Sets ◽  
Post Processing ◽  
Stone Size ◽  
Data Set ◽  
Detection Rates ◽  
Exact Test ◽  
Significant Difference ◽  
Exact Size

Purpose: Kidney, ureter, and bladder radiography (KUB) has frequently been used in suspected urolithiasis, but its performance is known to be lower than that of computed tomography (CT). This study aimed to investigate the diagnostic performance of digitally post-processed kidney ureter bladder radiography (KUB) in the detection of ureteral stones. Materials And Methods: Thirty patients who underwent digital KUB and CT were included in this retrospective study. The original digital KUB underwent post-processing that involved noise estimation, reduction, and whitening to improve the visibility of ureteral stones. Thus, 60 digital original or post-processed KUB images were obtained and ordered randomly for blinded review. After a period, a second review was performed after unblinding stone laterality. The detection rates were evaluated at both initial and second review, using CT as reference standard. The objective (size) and subjective (visibility) parameters of ureteral stones were analyzed. Fisher’s exact test was used to compare the detection sensitivity between the original and post-processed KUB data set. Visibility analysis was assessed with a paired t-test. Correlation of stone size between CT and digital KUB data sets was assessed with Pearson’s correlation test. Results: The detection rate was higher for most reviewers once stone laterality was provided and was non-significantly better for the post-processed KUB images (p > 0.05). There was no significant difference in stone size among CT and digital KUB data sets. In all reviews, visibility grade was higher in the post-processed KUB images, irrespective of whether stone laterality was provided. Conclusion: Digital post-processing of KUB yielded higher visibility of ureteral stones and could improve stone detection, especially when stone laterality was available. Thus, digitally post-processed KUB can be an excellent modality for detecting ureteral stones and measuring their exact size.

Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

Causal ascription of rework in building and civil engineering projects

2014 ◽  
Vol 21 (1) ◽  
pp. 111-126 ◽  
Author(s):  
Palaneeswaran Ekambaram ◽  
Peter E.D. Love ◽  
Mohan M. Kumaraswamy ◽  
Thomas S.T. Ng
Keyword(s):  
Construction Projects ◽  
Civil Engineering ◽  
Small Data ◽  
Data Set ◽  
Content Type ◽  
Management Measures ◽  
Significant Difference ◽  
Quantitative Impact ◽  
Engineering Projects

Purpose – Rework is an endemic problem in construction projects and has been identified as being a significant factor contributing cost and schedule overruns. Causal ascription is necessary to obtain knowledge about the underlying nature of rework so that appropriate prevention mechanisms can be put in place. The paper aims to discuss these issues. Design/methodology/approach – Using a supervised questionnaire survey and case-study interviews, data from 112 building and engineering projects about the sources and causes of rework in projects were obtained. A multivariate exploration was conducted to examine the underlying relationships between rework variables. Findings – The analysis revealed that there was a significant difference between rework causes for building and civil engineering projects. The set of associations explored in the analyses will be useful to develop a generic causal model to examine the quantitative impact of rework on project performance so that appropriate prevention strategies can be identified and developed. Research limitations/implications – The limitations include: small data set (112 projects), which include 75 from building and 37 from civil engineering projects. Practical implications – Meaningful insights into the rework occurrences in construction projects will pave pathways for rational mitigation and effective management measures. Originality/value – To date there has been limited empirical research that has sought to determine the causal ascription of rework, particularly in Hong Kong.

scholarly journals A Study on Emotional Intelligence among School Going Adolescents in Kolkata

2021 ◽  
pp. 47-58
Author(s):  
Bikash Chandra Ghorai ◽  
Samayita Kundu ◽  
Sunil Santra
Keyword(s):  
Emotional Intelligence ◽  
Sampling Technique ◽  
Cross Sectional Survey ◽  
School Students ◽  
Cross Sectional ◽  
Data Set ◽  
Intelligence Scale ◽  
Four Dimensions ◽  
Significant Difference ◽  
Understanding Emotions

The aim of the present study is to determine the level of emotional intelligence of school going adolescents; and to compare the emotional intelligence and its four dimensions/sub-factors i.e., understanding emotions, understanding motivation, empathy and handling relation of school going adolescents with respect to their gender, grade and board pattern of education. The present study was carried out on 288 higher secondary school students selected as sample from six schools of three different boards of education (viz. two WBCHSE, two CBSE and two ICSE) of Kolkata district in West Bengal using convenient sampling technique. This research is cross-sectional survey type study. The measuring tool in this research originally was of two-point emotional intelligence scale entitled as ‘Emotional Intelligence Scale (ESI – SANS) of Dr. A. K. Singh and Dr. S. Narain [1] which was translated in Bengali version by B. C. Ghorai and L. L. Mohakud [2]. After the initial exploratory analysis of the data, different statistical (descriptive and inferential) techniques are used to analyze the data set via SPSS 20. Results of the study revealed that there is no statistically significant difference in emotional intelligence and it’s sub-factors of school going adolescent with respect to their gender grade and board pattern of education. The findings provide a further need on how to more improve upon the emotional intelligence of school going adolescent. Implications and recommendations for developing emotional intelligence school going adolescent are discussed.

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