Random Sample Tests Research Articles

Our Current Misunderstanding of COVID-19 and the Valid Approach to Research: Should We Reopen the World Economy?

The study addresses issues related to the current epidemiological models being used to address the COVID-19 pandemic, and suggests the “curve” is a nebulous fairy tale. Furthermore, I argue that these models are based on invalid data and flawed assumptions. For example, epidemiologists are using a number of positive cases, identified in systematic data collection manners (e.g., primarily testing frontline workers and symptomatic people), as proxy measures for infection rate: I suggest that random sample testing, proportional to population density, would provide an exact measure of infection rate. Finally, I argue that we are not in a position to reopen the world economy until we have these data, we now need a twopronged approach through which we conduct random sample testing for both COVID-19 and COVID-19 antibodies, and that we are about to live in a new world in which physical distancing1 will be the norm.

Exploring and analysing single cell multi-omics data with VDJView

BackgroundSingle cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.ResultsWe developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.ConclusionsVDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview.

The discovery of potent immunostimulatory CpG-ODNs widely distributed in bacterial genomes.

Oligodeoxynucleotides containing unmethylated CpG dinucleotides (CpG-ODN) can be specifically recognized by Toll-like receptor 9 (TLR9), provoking innate immune responses. Designed according to this structural feature, many synthetic phosphorothioate CpG-ODNs successfully activate macrophages. However, it is difficult to find potent stimulatory CpG-DNA fragments in microbial genomes. Therefore, whether microbial CpG-DNA substantially contributes to infectious and immune diseases remains controversial. In this study, high-throughput scanning was carried out for thousands of bacterial genomes with bioinformatics tools to comprehensively evaluate the distribution of CpG-DNA fragments. A random sampling test was then performed to verify their immunostimulatory properties by experiments in vitro and in vivo. Natural TLR9-dependent and potent stimulatory CpG-DNA fragments were found in microbial genomes. Interestingly, highly conserved stimulatory CpG-DNA fragments were found in 16S and 23S rDNA sequences with multiple copies, while others were species-specific. Additionally, we found that the reported active motifs were mostly non-stimulatory in natural CpG fragments. This evidence indicates that the previous structural descriptions of functional CpG-ODNs are incomplete. Our study has assessed the distribution of microbial CpG-DNA fragments, and identified natural stimulatory CpG-DNA fragments. These findings provide a deeper understanding of CpG-ODN structures and new evidence for microbial DNA inflammatory function and pathogenicity.

Optimization of variable blank holder force in deep drawing based on support vector regression model and trust region

Blank holder force (BHF) is one of the important process parameters for successful sheet metal forming. Variable blank holder force (VBHF) that the BHF varies through the forming process is recognized as one of the advanced manufacturing technologies. Therefore, optimization of VBHF trajectory is a crucial issue in industries. One of the effective approaches to determine the VBHF trajectory is to use the surrogate modeling techniques. However, it is very inaccurate and time-consuming to determine the VBHF trajectory for successful sheet forming through surrogate-based optimization methods. Therefore, this paper proposes an improved surrogate-based optimization method by integration of support vector regression (SVR) and trust region strategy to optimize VBHF in deep drawing. First, a random sampling test of VBHF in deep drawing is designed and a SVR approximate model of VBHF under random sampling is developed. Then, a trust region algorithm is adopted to predict and control the accuracy of the SVR approximate model of VBHF. Response surface is repeatedly constructed and optimized that is adopted to identify the Pareto-frontier of VBHF. The validity of the proposed approach is examined through the comparison of numerical and experimental results. The results of this research provide a reliable reference for future efforts to optimize VBHF in deep drawing.

Analysis of Factors Giving the Opportunity for Implementation of Innovations on the Example of Manufacturing Enterprises in the Silesian Province

This paper analyzes the actions that improve innovativeness in production enterprises in the Silesian province. Innovation is one of the elements that allows to achieve a competitive advantage. It is justified to research various factors that are important in improving innovativeness. The research includes selected production enterprises in the Silesian province, adopting the descriptive statistics measures and statistic tests: random sample test, chi-square independence test and the non-parametric Kruskal-Wallis test based on a survey questionnaire. Some of the most important factors determining the possibilities of innovation by manufacturing companies were detected contacts with other enterprises, R&D centers and counseling institutions, competitive position of the company, and creating appropriate incentive systems.

IMPROVING LISTENING ABILITY BY ACTIVATING STUDENTS’ BEFORE KNOWLEDGE

This research paper aims at finding whether the students’ ability in listening can be improved by activating students’ previous knowledge. The research was conducted at English education study program of faculty of IKIP Siliwangi Bandung. The research used random sampling class and test by giving pretest and post test. Two classes are experimental class and control class, when the experimental class got a better score than control class.The experimental class was increased in listening ability. Most of the student’s of experimental class students’ stay in fair category.

Chinese writing development of Kindergarten students over 12 months

Purpose The purpose of this paper is to examine the writing development of Hong Kong kindergarten students over 12 months. They attended 18 kindergartens territory-wide and were followed from June 2002 to June 2003 for the collection of three waves of teacher-rated data at six-month intervals. Design/methodology/approach First, the construct validity of the translated and culturally adapted version of Morrow’s (2012) checklist which assesses writing development was confirmed, considering that the students attended Hong Kong kindergartens who wrote in the Chinese language. The multilevel analysis, which employed corrected measures captured through Wolfe and Chiu’s (1999a, 1999b) five-step Rasch scaling method for a common frame of reference, estimated the effects of the factors, namely, student age, gender, class level and schools. Findings The children’s progress over the second six months was also apparently much smaller than the first SIX months for this cohort. The dramatic slow-down in the second six-month period for both cohorts might be partly attributed to the peculiar arrangement of schooling at that time. Research limitations/implications The recommendation from this study is that random sampling and student test scores on writing need to be taken for the identification of the general trend of young children’s writing development in Hong Kong, as well as other Chinese communities alike. Originality/value The profile of the student’s emergent writing development at each six-month follow-up and over the 12 months was explored. Differences between the groups based on age, gender, class level and school in terms of student writing development on average were statistically significant.

Oil Rig Recognition Using Convolutional Neural Network on Sentinel-1 SAR Images

Recent advances in deep learning for automatic target recognition have inspired studies for maritime surveillance with synthetic aperture radar (SAR). In this context, oil rigs monitoring is of particular interest. There are offshore platforms of different types and sizes which poses challenges for classification methods. In this letter, we compare the recognition of oil rigs on Sentinel-1 SAR images, discriminating them from false alarms, through a convolutional neural network with a different number of layers named models VGG-16 and VGG-19. We used the Sentinel-1 GRD product, which has a resolution of about 20 m, with VV and VH channel image data sets separated to observe polarization influences on target recognition. The experiments show that the VH polarization image embedding contributes with the best classification values, with the mean global accuracy of fifty random sampling tests equal to 86.4% and 84.1% using the VGG-16 and VGG-19 architectures, respectively, to extract features and feed a logistic regression classifier method. Results suggest that these approaches are a useful alternative for maritime surveillance employing SAR images of medium resolution and large swaths. Furthermore, the database balancing for wind conditions did not provide significant contributions to the accuracy of classification algorithms.

The Associations of PMF1, ICAM1, AGT, TRIM65, FBF1, and ACOX1 Variants With Leukoaraiosis in Chinese Population.

Background: Leukoaraiosis (LA) is shown as white matter hyperintensities on T2-weighted magnetic resonance imaging brain scans. Together with candidate gene association studies (CGAS), multiple genome-wide association studies (GWAS) have reported large numbers of single nucleotide polymorphisms (SNPs) to be associated with LA in European populations. To date, no replication studies have been reported in independent Chinese samples. Methods: Here, we performed a candidate gene association study comprising 220 Chinese subjects with LA and 50 controls. Thirty-nine polymorphisms on 32 risk genes were selected from previous studies, and they were genotyped through matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Genetic association analysis was firstly performed in all subjects with LA. Then, the same analysis was conducted in the six random sampling cohorts of 50 LA patients, respectively. Data analyses on the associations of SNPs with LA risk were evaluated through Pearson’s χ2 and multivariate logistic regression tests. Results: We found that eight polymorphisms in six genes (PMF1, ICAM1, TRIM65, AGT, FBF1, and ACOX1) were significantly associated with LA in the genetic association tests. Except for those eight gene variants, 24 other polymorphisms were not found to be significantly associated with LA in general genetic model, dominant model, recessive model, or multiplicative model. Among those eight polymorphisms, rs2984613 in PMF1 showed significant association with LA in the cohort of 220 LA subjects, and such significant association remained in both general genetic model (OR: 0.262, 95% CI: 0.091–0.752, p adj = 0.030) and recessive model (OR: 0.323, 95% CI: 0.119–0.881, p adj = 0.038) when controlling for clinical variables. Seven other significant variants (rs5498 in ICAM1, rs699 in AGT, rs2305913 in FBF1, rs1135640 in ACOX1, and rs3760128, rs7214628, and rs7222757 in TRIM65) were identified in those six random sampling tests that were conducted in the adjusted cohorts of 50 LA patients. In addition, except for rs699 which showed detrimental effect and represented a risk variant for LA, seven other polymorphisms seemed to exert protective effects on LA and to reduce the risk of LA. It is necessary to confirm these associations in an independent cohort. Conclusions: This first replication study on multiple genes in an independent Chinese population did not replicate any risk polymorphisms for LA other than rs 699 in AGT but revealed the significantly negative associations of PMF1, ICAM1, TRIM65, FBF1, and ACOX1 polymorphisms with LA. It not only supported the strong ethnic differences in the genetics of LA but also indicated that those six identified genes may be involved in Chinese white matter lesions. Larger scales of CGAS and GWAS are necessary to confirm and decipher those ethnic-Han specific risk genes for LA in China.

The Effect of Principal Leadership Behaviour and Work Culture Towards Work Discipline Elementary School Teachers on District Minahasa

This This study aims to obtain the results of analysis and clear interpretation of: (1) The positive influence of principals' leadership on the work discipline of elementary school teachers in Minahasa District. (2) The positive influence of work culture on the work discipline of elementary school teachers in Minahasa District. (3) The positive influence of the principal's leadership and work culture simultaneously (together) on the work discipline of elementary school teachers in Minahasa District. Sampling techniques using simple random sampling and data testing techniques used validity test by calculating between scores in each statement with a total score, using the Pearson Product Moment formula and reliability testing using the Cronbach Alpha formula. Testing the analysis requirements used the normality test and linearity test. To prove the research hypothesis on each independent variable with the dependent variable using simple regression analysis. Whereas for the effect of jointly between independent variables and dependent variables using multiple regression analysis. The results of the analysis show that: (1) there is a positive influence on Principal Leadership and Work Discipline, (2) there is a positive influence on the work culture and work discipline, (3) there is a positive influence of Principal Leadership and Work Culture simultaneously (together ) with Work Discipline. Based on the results of the study it can be concluded that the work discipline of elementary school teachers in Minahasa Regency can be improved through improving the quality of the work culture and the function of leadership behavior. Keywords : Principal Leadership Behavior, Work Culture, Work Discipline. DOI : 10.7176/JEP/10-21-02 Publication date :July 31 st 2019

THE INFLUENCE OF EXCLUSIVE BREAST FEEDING TO THE INFANTS ’6-12 MONTHS OF AGE DEVELOPMENT IN CANDI MULYO JOMBANG

Infancy is a vital period in which physical and mental state of the baby becomes the foundation for baby development. This period must be optimized by keeping the baby’s health and its nutritional status by giving exclusive breastfeeding. This research was conducted by using an analytical design with a cross-sectional approach. The population of the research was 96 infants aged 6, 9, 12 as respondents and 77 respondents as a sample taken by using simple random sampling and statistical testing of Spearman’s rho. The research instrument was a questionnaire and KPSP. The data were analyzed by using some steps, they are editing, coding scoring, and tabulating. The result showed that respondents who are exclusively breastfed are 31 ( 40.3 % ) , the majority of respondents experienced a corresponding growth of 21 ( 58.5 %), on the contrary infants that are not exclusively breastfed by 46 ( 59.7 % ) experienced growth doubted 25 ( 44.1 % ) of the total that had been developed to deviate as much as 8 ( 10.4 % ). Statistical test results of Spearman 's rho obtained ρ value ( 0.001 ) < α (0.05 ) so that H0 rejected and H1 accepted. There was an influence of exclusive breastfeeding on the development of infants aged 6-12 months in Candimulyo Jombang.

Comparison between regression and artificial neural network for prediction model of flexibly reconfigurable roll forming process

Flexibly reconfigurable roll forming (FRRF) is a novel sheet metal forming technology in which sheet metal is shaped into the desired curvature using reconfigurable rollers and gaps. However, FRRF has a disadvantage in terms of predicting the forming result because this technology produces two-dimensional curved surfaces from one-dimensional curved lines. A previous study used regression analysis to predict the longitudinal curvature results of the FRRF process. In this study, a prediction model using machine learning was developed. A comparative investigation with an existing regression model was conducted using an artificial neural network (ANN) model based on machine learning. Sample data were obtained via numerical simulation, and the target surfaces were convex and saddle-shaped. To confirm the validity of each model, a goodness-of-fit test was performed. Further, to confirm the predictability of each model, the same amount of random data was extracted and a random sample test was performed. In the case of the saddle-shaped surface, the ANN model showed a predominantly high predictability, but in the case of the convex shape surface, the regression model performed better. The ANN model has potential to be used in practical applications such as skin structure forming for aircraft and shipbuilding.

The Influence of Customer Experience to Revisit Intenton in Pandawa Beach Attraction

One of the attractions in Bali to allure domestic and foreign visitors to come is Pandawa Beach. Located in Kutuh Village, Kuta Selatan sub-district, Badung regency, less than 3 kms from Nusa Dua Tourism Area and Uluwatu Temple, Pandawa Beach gives a different perpective of sight from the trimmed limestone cliffs terraced on the sides of the road, the ocean’s beauty and clean white sands garnished by stone carved statues. All of these constitute a potential view and offering experiences for the visitors. Therefore, the local authority along with the caretaker of Pandawa Beach have implemented a concept of customer experience in order to achieved the increasing level of revisit intention to Pandawa Beach. The aim of this research is to find out the effect of customer experience to revisit intention in Pandawa Beach Tourist Attraction, using quantitative descriptive method. As many as 100 respondents was taken by simple random sampling test and surveyed. The results of the study are due to between customer experience and revisit intention .Customer experience significant impact on revisit intention .Of the relations between experiences visitors with their respective interest visit back marked positive. This proves that the better experience of the visitors will be interest visit back.

A modified Logit model for assessment and validation of debris-flow susceptibility

The accuracy of a debris-flow susceptibility assessment model can not be achieved to an acceptable level without rigorously selecting/processing model input parameters and reliably validating the results with ground truth. In this study, a modified logistic regression model was applied to examine the debris-flow susceptibility in the semi-arid mountainous areas of the southeast Tibetan Plateau, taking the case of the Benzilan-Changbo segment of the Jinsha River. The logistic regression model validation was supported by a field investigation and interpreting aerial photographs. The frequency ratio method was adapted for the sensitivity analysis of ten predisposing factors, including a factor of human activities that has never been considered in previous studies for the selected study area. The model with the highest prediction accuracy (83%) was obtained by comparing 55 possible combinations of the ten factors evaluated using a receiver operating characteristic curve technique. Composite analysis and random sample testing were also conducted to verify the reliability of the susceptibility assessment. The prediction results showed that the actual debris-flow area accounted for 92% of the total predicted debris-flow area. In summary, this study reveals that: (1) the highest susceptibility area was mainly distributed in the northeast and middle riparian zones, partially scattered in the southwest. Human activity was found to be closely related to the probability of debris flow occurrences; (2) under the two situations of independent and index combination participation evaluation, the performances of the same related factors showed largely different effects on the assessment results, which reflected the interrelation and interaction of the factors causing the debris flow; (3) the accuracy of the modified logistic regression model increased by 15%, when compared with the index entropy model in a previous study, and the reasons for this were discussed in the current study. The modified model, along with its enhanced validation process, could be easily extended to other regional mass movement susceptibility analyses.

FAKTOR YANG BERHUBUNGAN DENGAN PENGGUNAAN ALAT KONTRASEPSI PADA PASANGAN USIA SUBUR DI DESA LOHBENER KABUPATEN INDRAMAYU

Family Planning Program is an effort to measure the number and distance of child that is desired. In order to achieve this program, several options are made to prevent or delay pregnancy through the implementation of quality family planning services. The purpose of this study was to determine the factors associated with the use of contraception in couples of childbearing age in Lohbener Village, Indramayu Regency. This study applied observational-analytic study, with a cross sectional study design. The population in the study were all women of childbearing age aged 15-49 years and married, while the sample in this study were parts of women of childbearing age aged 15-49 years and married in Lohbener village, Indramayu Regency. The sampling technique was conducted using simple random sampling and logistic regression test. Based on the results of logistic regression test, factors related to the use of contraception in of women of childbearing age in Lohbener Village, Indramayu Regency were husband's support (p value = 0.000; CI: 4.229-70.362), and knowledge (p value = 0.000; CI: 3.012-34.233 ), while unrelated factors were work status (p value = 0.577; CI: 0.214-15.902), parity (p value = 0.319; CI: 0.193-1.710), and access to family planning services (p value = 0.984; CI: 0.315-3.250) with the use of contraceptives in Lohbener Village, Indramayu Regency. Therefore it is advised that women of childbearing ages over ≥ 20 years to pay more concern to their health through good family planning such as regulating the number of births, sparing pregnancies, and more actively consulting to the health workers to conduct family planning programs.

Indigen Bacteria from Spent Bleaching Earth Waste as A Removal Agent of Fe and Cu

The most pollution produced by oil palm factory is Spent Bleaching Earth, because the waste contains residue of oil and dangerous metal that enable bacteria to live hence the research was done to get bacteria of Fe and Cu metal as bioremoval agent. Waste sampling method is random sampling, then purification, selection and potency test. Bacteria that pass potency test on metal containing media are identified and characterized by their genus. The result of the research indicated that the indigenous bacteria genus for good accumulation of Fe metal is Salmonella sp (B7) and for Cu metal is Escherichia sp (B1).

RETRACTED ARTICLE: Application of Monte Carlo calculation method based on special graph in medical imaging

With the rapid development of medical imaging technology and information technology, digital medical image acquisition equipment is constantly updated, especially the rise of proton medical technology, which has higher requirements on human diseased cell imaging technology. In this paper, based on the image data acquired by the large aperture CT, the Monte Carlo calculation and analysis of edge-blurred electronic density images have carried out to achieve accurate diagnosis and provide a new method for efficient pathological treatment. This article made a preliminary study and analysis on the application of Monte Carlo method in radio surgical treatment planning. Monte Carlo method, which is known as random sampling or statistical test methods, can truly simulate the actual physical process, problem solving and actual in good compliance. It can be very successful results. Combined with the image processing method, this paper carries out the detection methods of image pre-processing, image segmentation, feature extraction, classification and recognition to the lesion area of human body to realize the effective recognition of the special lesion image area. Experimental results show that the edge of lesion area has obtained by image segmentation, contour extraction and Monte-Carlo calculation. Particle-space information has taken as input. Based on Monte Carlo method, the space-absorbed dose in phantom is analysed distributed. By using the computerized liver cancer diagnosis technology, the doctor’s working pressure and labour intensity are reduced, the doctor’s working efficiency is improved, the accuracy of the diagnosis is improved, the hospital saves many expenses and the patient’s burden is reduced. Therefore, the research of computer-aided diagnosis has important significance and application value.

Fault diagnosis method for water quality monitoring and control equipment in aquaculture based on multiple SVM combined with D-S evidence theory

A water quality monitoring and control (WQMC) system is an important tool designed to maintain good water quality in aquaculture. A variety of events could cause WQMC equipment to malfunction. Such a problem would in turn result in the generation of unreliable monitoring information and reduced water quality control. The high-dimensional dataset generated by the WQMC equipment makes fault identification difficult. To solve the problem of fault identification, this paper reduced the effects of autocorrelation on the variables and determined feature space based on dynamic principal component analysis (DPCA). Based on cross-correlation analysis, 24 support vector machine (SVM) classifiers were developed for the multi-SVM model. A vote procedure was proposed to identify fault types and conflicts. To solve the conflicts and reduce incorrect diagnosis results, an amendment based on D-S theory determined which fault contributed more to causing the corresponding symptoms. Experimental results showed that the single-pass accuracy of the multi-SVM model in random sample tests varied from 90% to 94% and the combined method could effectively solve the conflicts. Furthermore, the fault identification accuracy could be improved by 3–5%. Incorrect fault diagnosis results remained, and the successful amendment ratio required further improvement. However, the proposed method was helpful for the maintenance and management of WQMC equipment.

An Online SOC and SOH Estimation Model for Lithium-Ion Batteries

The monitoring and prognosis of cell degradation in lithium-ion (Li-ion) batteries are essential for assuring the reliability and safety of electric and hybrid vehicles. This paper aims to develop a reliable and accurate model for online, simultaneous state-of-charge (SOC) and state-of-health (SOH) estimations of Li-ion batteries. Through the analysis of battery cycle-life test data, the instantaneous discharging voltage (V) and its unit time voltage drop, V′, are proposed as the model parameters for the SOC equation. The SOH equation is found to have a linear relationship with 1/V′ times the modification factor, which is a function of SOC. Four batteries are tested in the laboratory, and the data are regressed for the model coefficients. The results show that the model built upon the data from one single cell is able to estimate the SOC and SOH of the three other cells within a 5% error bound. The derived model is also proven to be robust. A random sampling test to simulate the online real-time SOC and SOH estimation proves that this model is accurate and can be potentially used in an electric vehicle battery management system (BMS).

Adaptive Data Wiping Scheme with Adjustable Parameters for Ext4 File System

Data wiping is a useful technology which can prevent possible data recovery in a file system. But the growth of the amount of data usually leads to a decline in wiping efficiency. In order to make improvement, a novel data wiping scheme for Ext4 file system DWSE is proposed. It includes two proposed algorithms for wiping files and free space adaptively. According to a rate of rest blocks which is specified by users, the file wiping algorithm WFile tries to clean part of a selected file for saving time. The free space wiping algorithm WFree tries to speed up the process of cleaning dirty blocks by employing a random sampling and hypothesis testing method with two adjustable rates which represent status and content of a block group. A journal cleaning algorithm CleanJ is also proposed which tries to clean old records by creating and deleting temporary files for preventing data recovery from a journal file. With the help of parameters, users can wipe their data in a balanced way between security and efficiency. Several experiments are performed on our scheme. The experimental results show that our scheme can wipe files and free space in different security and efficiency with different parameters. Our scheme can achieve higher security and efficiency than other two data wiping schemes.