An Introduction to DNA Microarrays

In smart grid, more data will be collected at finer granularity. It will promote the development of power system control and innovation technology. With the advantages of cloud computing technology are becoming increasingly prominent, it is gradually entering into power industry research. This paper focuses on cloud-based data processing and analysis techniques of user side in smart grid. To prove that the integration of cloud computing, smart grid, data analysis technique is practical, we introduced the cloud platform architecture designed for data processing and its key technologies first. Then, main sections of cloud-based data processing, including data collection, storage, analysis and visualization are discussed. Especially, based on smart grid scenes, data analysis methods in data analysis section are described in detail.

BackgroundHigh-throughput, parallel gene expression analysis by means of microarray technology has become a widely used technique in recent years. There are currently two main dye-labelling strategies for microarray studies based on custom-spotted cDNA or oligonucleotides arrays: (I) Dye-labelling of a single target sample with a particular dye, followed by subsequent hybridisation to a single microarray slide, (II) Dye-labelling of two different target samples with two different dyes, followed by subsequent co-hybridisation to a single microarray slide. The two dyes most frequently used for either method are Cy3 and Cy5. We propose and evaluate a novel experiment set-up utilising three differently labelled targets co-hybridised to one microarray slide. In addition to Cy3 and Cy5, this incorporates Alexa 594 as a third dye-label. We evaluate this approach in line with current data processing and analysis techniques for microarrays, and run separate analyses on Alexa 594 used in single-target, dual-target and the intended triple-target experiment set-ups (a total of 18 microarray slides). We follow this by pointing out practical applications and suitable analysis methods, and conclude that triple-target microarray experiments can add value to microarray research by reducing material costs for arrays and related processes, and by increasing the number of options for pragmatic experiment design.ResultsThe addition of Alexa 594 as a dye-label for an additional – third – target sample works within the framework of more commonplace Cy5/Cy3 labelled target sample combinations. Standard normalisation methods are still applicable, and the resulting data can be expected to allow identification of expression differences in a biological experiment, given sufficient levels of biological replication (as is necessary for most microarray experiments).ConclusionThe use of three dye-labelled target samples can be a valuable addition to the standard repertoire of microarray experiment designs. The method enables direct comparison between two experimental populations as well as measuring these two populations in relation to a third reference sample, allowing comparisons within the slide and across slides. These benefits are only offset by the added level of consideration required in the experimental design and data processing of a triple-target study design. Common methods for data processing and analysis are still applicable, but there is scope for the development of custom models for triple-target data. In summary, we do not consider the triple-target approach to be a new standard, but a valuable addition to the existing microarray study toolkit.

Biomedical signal processing and data analysis play pivotal roles in the advanced medical expert system solutions. Signal processing tools are able to diminish the potential artifact effects and improve the anticipative signal quality. Data analysis techniques can assist in reducing redundant data dimensions and extracting dominant features associated with pathological status. Recent computational methods have greatly improved the effectiveness of signal processing and data analysis, to support the efficient point-of-care diagnosis and accurate medical decision-making. This editorial article highlights the research works published in the special issue of Computational Methods for Physiological Signal Processing and Data Analysis. The context introduces three deep learning applications in epileptic seizure detection, human exercise intensity analysis, and lung nodule CT image segmentation, respectively. The article also summarizes the research works on detection of event-related potential in the single-trial electroencephalogram (EEG) signals during the auditory tests, along with the methodology on estimating the generalized exponential distribution parameters using the simulated and real data produced under the Type I generalized progressive hybrid censoring schemes. The article concludes with perspectives and discussions on future trends in biomedical signal processing and data analysis technologies.

<div class="htmlview paragraph">Significant reductions in motor vehicle emissions are possible through the implementation of inspection and maintenance (I/M) programs. However, the potential benefits of I/M are obviously not achieved when specific inspection requirements are ignored or improperly performed. In addition, I/M benefits may be substantially reduced when improper repair procedures are used on vehicles which fail the test.</div> <div class="htmlview paragraph">In order for the “theoretical” benefits of I/M to be achieved, certain program design and enforcement procedures are necessary. The use of instrumentation and data analysis methods capable of identifying individuals who are improperly performing inspections and repairs is critical. Under the I/M programs developed for use in the state of Alaska, data analysis techniques are employed which identify mechanics who are reporting invalid information about the vehicles they inspect, failing to repair an excessive number of vehicles, or reporting visual inspection failure rates that are much higher or much lower than the norm. Similar data analysis methods are being used under the California I/M program.</div> <div class="htmlview paragraph">Based on analyses of currently operating programs, it is clear that modified instrumentation and data analysis techniques can further enhance the ability to identify inspectors and mechanics who need additional instruction or other remedial action. Progress in this area will significantly affect the air quality benefits of I/M.</div>

The real-time system of the JT-60 has a cascade structure with the major and the minor control loops. The multi-variable feedback control is adopted in the major control loop in order to adjust non-linear effects which will appear in the high-/beta/region. By applying the concept of 'phase shift control' to the control program of the major control loop, a flexible tokamak discharge control is expected to be performed with taking account of machine protection.

Chapter 6 - Naturalistic Driving Studies and Data Coding and Analysis Techniques

The measurement and data analysis used to evaluate highway roughness

Spectroscopic ellipsometry is an important thin film metrology technique. It offers non-destructive and non-invasive materials characterization, including measurement of the complex refractive index and other microstructural properties that affect the material optical response. It has been applied to many important ultra large scale integration (ULSI) thin-film metrology needs; as it is applicable to semiconductors, dielectrics, metals, organic films, and more. Recent advances have broadened the impact of ellipsometry for many applications. New instruments are extending the spectral range to both shorter and longer wavelengths. High-speed spectroscopic measurements can be accomplished with diode-array technology. In addition to hardware advances, new data analysis techniques have extended the capabilities of spectroscopic ellipsometry. The advanced capabilities of spectroscopic ellipsometry will be reviewed, including the importance of new spectral ranges in the vacuum ultra-violet (VUV) and infrared (IR). The merit of fast measurements for in-line and in-situ measurements is covered. New data analysis methods are discussed, with their possible applications toward ULSI thin film metrology. In short, this paper reviews the progress of hardware and data analysis innovations and their impact on ULSI thin film metrology.

This research aims to analyze and determine the influence of competence, communication and compensation on employee performance in a company. This research was conducted using a quantitative approach and involved respondents consisting of employees at various levels of positions who work at companies in the district. Jagakarsa, South Jakarta. Data was collected through a questionnaire with a total of 22 questions and a total of 100 respondents which could measure the level of competency, communication, compensation and employee performance. The data analysis technique used in this research is SEM PLS (Structural Equation Modeling - Partial Least Squares), which was carried out using SmartPLS 4.0 software. The data analysis methods used in this research include measurement model analysis, structural model analysis, and direct influence hypothesis testing. The results of data analysis show that employee competency has a significant positive influence on employee performance. Communication also has a significant positive influence on employee performance. Effective and open communication allows clear and timely information to have a positive and significant impact on improving employee performance. And the compensation variable was also found to have a significant positive influence on employee performance. These findings provide important implications for human resource management and organizational practitioners in designing policies and programs related to competency development, improving communication, and providing adequate compensation for employees so that employees can provide reciprocity in the form of maximum performance for the company.

PT Jasamarga Tollroad Operator (JMTO) ia trusted by the company to handle toll road operations which include transaction services at toll gates and toll road services. PT Jasamarga Tollroad Operator (JMTO) is responsible for smoothness, comfort, and safety on toll roads. Good coaching to all employees will improve employee performance. This study aims to determine the effect of compensation, motivation, and job involvement on the performance of employees of PT Jasamarga Tollroad Operator (JMTO) Semarang Branch. The issue raised in this study is the decline in employee performance at PT Jasamarga Tollroad Operator (JMTO) which is indicated by the existence of work aspects that do not meet minimum service standards, which will have an impact on decreasing the quality of service to toll road users. The type of research used in this research is descriptive quantitative with a census sampling method using a questionnaire with a population of 157 respondents, with a Likert scale to measure each variable. Data analysis technique used is primary data using correlation test, linear regression, multiple. Methods of data analysis using SPSS version 24.00. The results obtained from this study are that the t-value of -0.440 is smaller than the t-table of 1.975 and the significance value of 0.687 is greater than 0.005. This shows that compensation has no effect on the performance of employees of PT Jasamarga Tollroad Operator (JMTO). Motivation obtained t value 3.493 greater than t table 1.975 with a significance value of 0.001 less than 0.005. meaning that the motivational variable affects performance. variable job involvement obtained t value of 9.506 greater than t table 1.975 with a significance value <0.001 less than 0.005. shows that the variable job involvement affects the performance of employees of PT Jasamarga Tollroad Operator (JMTO). Simultaneously the compensation, motivation and job involvement variables obtained results 63.385 greater than table 2.66 with a significance level of 0.001 less than 0.005. this shows that the variables of compensation, motivation and job involvement simultaneously have a significant effect on the performance of employees of PT Jasamarga Tollroad Operator (JMTO).

Research is an integral part of academics as well as innovation as new grounds cannot be achieved except through research work. It is imperative that a research work must be properly analysed and presented so as to enable readers and researchers have an adequate understanding of the work. This study will examine research as an important component of any discipline which is critical to the growth and development of any profession or discipline. It will provide and produce knowledge which is needed for understanding and combined with skills to lead to effective action. It is also a systematic, objectives analysis and record of controlled observation that may lead to the development of generalization, principles, theories resulting in prediction and ultimate control of many events that may have consequences or causes of specific activities. Through a survey research approach, combining the analysis of a research work, the study will adopt a cross- sectional survey research description and the sample size will be based on the concept of data collected and the data analysed. Various techniques of data collection will be examined and the study will further examine various methods or techniques of data analysis and various methods of presenting data received and processed in the course of the research work with the sole aim of contributing towards the body of the knowledge. The research will be carried out in higher institutions in the south-western part of Nigeria. Keywords: Research, research methodology, analysis of research work, data analysis

S100P-Binding Protein, S100PBP, Mediates Adhesion through Regulation of Cathepsin Z in Pancreatic Cancer Cells

Plant metabolomics is an important research area in plant science. Chemometrics is a useful tool for plant metabolomic data analysis and processing. Among them, high-order chemometrics represented by tensor modeling provides a new and promising technical method for the analysis of complex multi-way plant metabolomics data. This paper systematically reviews different tensor methods widely applied to the analysis of complex plant metabolomic data. The advantages and disadvantages as well as the latest methodological advances of tensor models are reviewed and summarized. At the same time, application of different tensor methods in solving plant science problems are also reviewed and discussed. The reviewed applications of tensor methods in plant metabolomics cover a wide range of important plant science topics including plant gene mutation and phenotype, plant disease and resistance, plant pharmacology and nutrition analysis, and plant products ingredient characterization and quality evaluation. It is evident from the review that tensor methods significantly promote the automated and intelligent process of plant metabolomics analysis and profoundly affect the paradigm of plant science research. To the best of our knowledge, this is the first review to systematically summarize the tensor analysis methods in plant metabolomic data analysis.

This book is a collective work by several leading scientists, analysts, engineers, mathematicians and statisticians, who have been working at the forefront of data analysis and related applications, arising from data science, operations research, engineering, machine learning or statistics. Data Analysis and Related Applications 5 represents a cross-section of current concerns and research interests in the above scientific areas. The collected material has been divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.

The focus of this chapter is on methods of data collection and analysis for the assessment of learning processes and complex performance, the last part of the empirical cycle after theory development and experimental design. In the introduction (van Gog and Paas), the general background and the relation between the chapter sections are briefly described. The section by Savenye, Robinson, Niemczyk, and Atkinson focuses on methods of data collection and analysis for assessment of individual learning processes, whereas the section by Johnson and O’Connor is concerned with methods for assessment of group learning processes. The chapter section by van Gog, Rikers, and Ayres discusses the assessment of complex performance, and the final chapter section by Duley, Ward, Szalma, and Hancock is concerned with setting up laboratories to measure learning and complex performance.