If the true value is zero, there’s a 5% chance of getting statistical signi icance: the Type I error rate, or rate of false positives or false alarms. There’s also a chance that the smallest worthwhile true value will produce an observed value that is not statistically signi icant: the Type II error rate, or rate of false negatives or failed alarms. The type II error is related to the size of samples in the research. In the old-fashioned approach to research design, we are supposed to have enough subjects to make a Type II error rate of 20%: that is, our study is supposed to have a power of 80% to detect the smallest worthwhile effect. If we look at lots of effects in a study, there’s an increased chance being wrong about at least one of them. Old-fashioned statisticians like to control this in lation of the Type I error rate within an ANOVA to make sure the increased chance is kept to 5%. This approach is misguided.
and multisite project clinical teams utilize comprehensive age- and disease-specific measures that are implemented as widely as possible and checked regularly for reliability. PBE data are merged into a central study database for analysis and hypothesis testing. Study findings are then implemented into clinical practice for validation testing with the ultimate goal of integration into standard care. PBE studies are designed to improve on traditional observational studies by 1) examining large, diverse patient populations; 2) involving clinicians in the research design and datacollection; 3) using carefully selected patient characteristics for analysis to avoid bias; and 4) standardizing datacollection and treatment documentation at all research sites. PBE methodology is ideal for conducting “pragmatic” trials that are designed to measure the overall benefit produced by a treatment in a naturalistic clinical setting. 9 Reviews in the pain medicine literature indicate
Normally, propagation model tuning measurements are carried out if planning is done for a new network and, if there is an area with changes in the propagation environment such as new buildings, new roads, or else a new frequency band is taken into consideration . It was mentioned in the previous chapter that statistical models are based on measurement data and have high computational efficiency as opposed to deterministic models. Practically, the accuracy of statistical models depends not only on the accuracy of the measurements, but also on the similarities between the propagation environments of the area where the measurement campaign is performed and the environment that the calibrated model is to be applied. To obtain such data, radio frequency (RF) measurements campaigns were performed in sub-urban region of Dehradun for various sites that contained buildings and vegetation.
to the datacollection and analysis study of programming language. We identify general developments evaluating trainee and specialist programmers, programming awareness and approaches, programmer creations and conceptions, and object oriented versus procedural programming. (We do not cover research relating specially to other programming style). The main focus of the review is on programming trainee and topics relating to datacollection and analysis. Various problems practiced trainee are acknowledged, as well as problem raise relating to various programming language which is most useful programming language of entire programming languages, to algorithmic complexity in certain language features, to the “Weakness” of trainees knowledge and so on. We reviews this materializes and give an opinion few practical suggestions for future work. We implicate that the key issue that materialize is difference of powerful and in- powerful trainees.
Various studies emphasize that online job adverts are not archived for later use (Kenechukwu, 2010; Mathews and Pardue, 2009; Reeves and Bellardo Hahn, 2010). The lack of centralised online archiving presents a challenge for both types of study. Although it may be possible to retrieve archived online job adverts after their closing dates, most job adverts would be removed from websites within a limited time period. The disadvantage of conducting a study on current jobs is that data need to be collected at least fortnightly. This means that census points for datacollection should be set.
This paper presents a systematic investigation comparing the proliferating number of lifecycle models that have been generated in the area of research support and researchdata management, both in the peer reviewed and practitioner literatures. The analysis of the models revealed the very differing perspectives on research that are current and the different value of these, as well as reminding us that no model can capture a comprehensive viewpoint. On a practical level, the analysis will help practitioners select or design models appropriate to the task at hand. A number of radically alternative visualisations/metaphors were also considered, such as the perspective held by many researchers of research as a transformational journey (and that might be best represented as a spiral) and the rather discontinuous journey of data itself, which could be visualised in subway map form. Given the complexity of both research and data our discussion has revealed both the flexibility and some of the perils in over-reliance on this one metaphor. Burgi and Roos (2003) suggest that when dealing with complex concepts, multiple metaphors are needed to avoid being trapped into simplistic assumptions. The lifecycle idea is an extremely useful metaphor, but it tends to encourage thinking that research processes are highly purposive, unidirectional, serial and occurring in a closed system. Research is often not like this, and the analysis has exposed the limited thinking created by such an assumption. The lifecycle model also often implies a repeated cycle when there is no real basis for this. The conclusion must to be suggest a need to add other visualisations for research to our repertoire of conceptual models. The knowledge spiral and the data journey map are just two such examples that reveal that viewing research through different metaphors enriches our understanding. This is important theoretically because LIS needs to develop a convincing
ABSTRACT: This article is a Proposal which provides a step-by-step guideline for ensuring a systematic defect prevention process and introduces a quantitative approach to measure the effectiveness of the process through a scoring model. It starts with identification of potential causes that usually impacts the defect prevention effectiveness. The proposed solution takes care of the most vital or critical causes as identified by the fish-bone-diagram analysis. The overall analysis method is segregated among five steps – fixing timeline, defect datacollection, analysis technique, reviewprocess and reporting process. Each step is elaborated further with introduction of its own parameters. Especially, the reviewprocess introduces the scoring model on different aspects of defect prevention reporting which generates RAG (Red-Amber-Green) score for each independent entity. This RAG scoring is very helpful in portraying the current status of any project to senior management and helps in accurate judgment of improvement scope for the betterment of delivery quality and customer satisfaction.
The systems described in the articles focus on a wide variety of different research areas and applications. Gulcher et al. presented a system for collecting researchdata and biospecimens for disease-based gene discovery projects that is supervised by the Data Protection Com- mission of Iceland  (see also ). Pommerening et al. described an infrastructure which enables longitu- dinal studies involving medical data, genetic data and data for managing collections of biomaterials . Eggert et al. presented an approach for collecting data and biomaterial for a research project on Parkinson’s disease . Angelow et al. described a solution for cen- tral biosample and data management in a project investi- gating inflammatory cardiomyopathy . The approach presented by Spitzer et al. utilizes pseudonymization to secure a web-based teleradiology platform for exchan- ging digital images between authorized users . Dangl et al. have implemented a solution for pseudonymization in the context of an IT-infrastructure for biospecimen collection and management in an academic medical center . Neubauer et al. presented a solution, in which smart cards allow patients to control the re-iden- tification process . Benzschawel and Da Silveira de- veloped a multi-level privacy protection scheme for a national eHealth platform . Demiroglu et al. de- scribed a system for a large-scale research project in the area of psychiatric genetics . Majchrzak and Schmitt described a web-based documentation system for long term observations of patients with nephro- nophtisis . Aamot et al. presented a system which implements sample and data management in transla- tional research for oncology patients . Finally, we have presented a generic solution for pseudonymized data and biosample collection which has been used to implement two research registries .
The writing of this research paper was partially driven by the need to revive the collection of climatic data initiated in 1993 but stopped in 2004 by the Solar Energy Application Laboratory (SEAL) of the Mechanical Engineering Department of KNUST. Weather data sets were collected by SEAL by employing weather monitoring equipment such as a propeller anemometer, radiometers for both global and diffuse irradiation, air-temperature/ relative humidity sensor and a rain gauge which were all manufactured by Kipp and Zonen. The climatic data collected by SEAL was obtained at a height of about 7 m. An annual average wind speed of about 1.5 m/s was recorded at the project site (the roof top of the building housing SEAL). This paper collected real wind data on two principal characteristics of wind namely wind speed and wind direction at a recording site which was located on top of the new classroom block of College of Engineering (COE) on KNUST campus at a height of 20 m.
Abstract: The objective of this paper is to present an application of design of experiments in which students learn how to get real data with application to a case using the catapult, and generate their statistical analysis through software, in order to have a great reliability, at the work development. The variation factors are selected between maximum and minimum levels accepted by catapult. The experimenting has showed. The results of the experiment are collected connected to the desired range, they are presented in tables and interaction graphs and Pareto graph. Doing the experiments it has been showed that not all variables of the catapult initially considered affect the quality of the result of the experiment. That is, for adjusting the bands considered only one factor has a significant effect on the quality of the experiment, it can be stated that there is no need to set a specific value of the catapult, but rather a range of values within which the experiment will have good performance.
Wireless Sensor Networks (WSN) contains number of sensor nodes those are scattered in an areas as well as batteries as a power source. These nodes have sensing unit, data processing unit as well as communication components which creates concept of sensor networks depending on collaborative effort of huge amount of nodes. Sensor nodes like this can be scattered in various places like home, military, science as well as organization for various applications like transportation, health care, disaster recovery, warfare, security, industrial and building automation, also in exploration space. From huge number of the applications phenomena monitoring is the main areas in WSNs. In such networks one can query the physical quantities of the environment.
We thank our Hudson Valley Healing Arts Center research team: Haley Moss Dillon, Sonja Siderias, Heather Orza, and Renee Nelson for their assistance, as well as Aron G Wie- gand, Egamaria Alacam, and Connor Duncan who assisted us with data input. We acknowledge with thanks the Bay Area Lyme (BAL) Foundation and the MSIDS Research Foundation (MRF) for providing us research grants for the data mining portion of this study. Dr Richard I Horowitz would also like to express his appreciation to his colleagues and subcommittee members on the HHS Tick-Borne Dis- ease Working Group for their dedication and expertise in the diagnosis and treatment of tick-borne disorders. The views expressed are those of Dr Richard I Horowitz and do not represent the views of the HHS Tick-Borne Disease Working Group. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript; and in the decision to publish the results.
The use of OFCS generally allows for the transmission of large amounts of data at high speeds for long distance transmission. A detailed investigation of EDFA was given in three principal levels; first is the EDFA with WDM and DWDM technique in order to achieve higher bit rates. Second level is the Theoretical analysis of EDFA, where it is necessary to understand the physical meaning behind the amplification. The third level is the presentation of various configurations and their performance parameters related to different structures. These parameters need to be controlled to get higher gain and lowest NF.By increasing the total pump power, the transmission distance can be increased. On the other hand increasing the total injected pump power increases the non-linear effects of the transmission fiber, which degrades the system performance. Finally researches are expected to focus on reducing the noise figure at high pump powers.
the most advantageous and useful computational tools for a multiplicity of signal and image processing applications. Wavelet transforms are mainly used for images to reduce unwanted noise and blurring . Wavelet transform has emerged as most powerful tool for both data and image compression. Wavelet transform performs multi resolution image analysis. The DWT has successfully been used in many image processing applications including noise reduction, edge detection, and compression. Indeed, the DWT is an efficient decomposition of signals into lower resolution and details. From the deterministic image processing point of view, DWT may be viewed as successive low-pass and high-pass filtering of the discrete time-domain signal . In 2D image, the images are generally considered to be matrices with N rows and M columns. In wavelet transform, the decomposition of a particular image consists of two parts, one is lower frequency or approximation of an image (scaling function) and an other is higher frequency or detailed part of an image (wavelet function). Figure 6 explains Wavelet Filter decomposition of an image where four different sub-images are obtained; the approximation (LL), the vertical detail (LH), the horizontal detail (HL) and the diagonal detail (HH).
A Clinical Data Management System or CDMS is used in clinical research to manage the data of a clinical trial. The clinical trial data gathered at the investigator site in the case report form are stored in the CDMS. To reduce the possibility of errors due to human entry, the systems employ different means to verify the entry. The most popular method contracted to perform all the administrative work on a clinical trial. It recruits participating researchers, trains them, provides them with supplies, coordinates study administration and datacollection, sets up meetings, monitors the sites for compliance with the clinical protocol, and ensures that the sponsor receives „clea n‟ data from every site. Recently, site management organizations have also been hired to coordinate with the CRO to ensure rapid IRB/IEC approval and faster site initiation and patient recruitment and patient recruitment. 23 Ethical Conduct
The term "Big data" is used for huge volume of data sets whose size is so large that a normal software tool cannot collect, arrange and process it within a certain time limit. 3Vs (volume, variety and velocity) are three basic blocks of big data. Volume refers to the amount of data, variety refers to the number of types of data and velocity refers to the frequency of data processing. An Opinion is a judgment or belief a majority of people. Sentiment analysis, is a natural language processing tool to find public mood about a product or topic. The tool used for Opinion Mining processes a collection of search results for a given product,generating product attributes (quality, features etc.) and aggregating opinion.OM is automatic extraction of knowledge from others opinions on a particular topic/problem. Opinion Mining is beneficial for strategizing organization’s marketing campaigns by studying the purchasing patterns of the people of particular region which helps the organization to get the insights of trending products.
de Sanidad y Política Social Región de Murcia; Sección de Ordenación e Inspección Farmacéutica Departamento de Salud; Comité Ético de Investigación Clínica del Hospital Universitario del Río Hortega de Valladolid, Consejería de Sanidad, Dirección General de Salus Pública, Junta de Castilla León; Comissão de Ética para a Saúde (CES), Centro Hospitalar de Lisboa Ocidental, EPE; Comissão de Ética para a Saúde (CES), Centro Hospitalar do Porto, E.P.E.; Comissão de Ética para a Saúde (CES), Centro Hospitalar Lisboa Central, EPE; Comissão de Ética para a Saúde (CES), Hospital Garcia de Orta, EPE; Comissão de Ética para a Saúde (CES), Centro Hospitalar de São João, EPE; Comissão de Ética para a Saúde (CES), Hospital Professor Doutor Fernando Fonseca, EPE; Comissão de Ética para a Saúde (CES), Centro Hospitalar do Algarve, EPE (Unidade de Faro); LUHS Kaunas Regional Biomedical Research Ethics Committee; Paula Stradi ņ a kl ī nisk ā s universit ā tes slimn ī cas, Att ī st ī bas biedr ī bas Kl ī nisk ā s izp ē tes Ē tikas komiteja, Ethics Committee for Clinical Research; Komisija Republike Slovenije za medicinsko etiko; Comitato Etico Indipendente Presso La Fondazione Ptv Policlinico Tor Vergata Di Roma;
economy through innovations by entrepreneurs inventing new products or new production processes. Additionally, they found that entrepreneurship could affect economic growth in several other ways as well, such as increasing competition by the increase of productivity or introducing variations of existing products and services in the market in order to develop the knowledge about what consumers want and what is technically feasible. Furthermore, Franco & Haase (2009) consider entrepreneurship as an important influence on the economy as well. Their study, based on data from 8 Portuguese small and medium sized enterprises (SME), shows that this type of business is of great significance to the economic development of a country in terms of job creation and added value. Job creation by small businesses is done in two different ways: through creation of new businesses and through expansion of existing enterprises (Liedholm, 2002). To put it in numbers, in the European Union more than 99% of the existing firms are SME, which account for two-third of the jobs in the private sector and more than 50% of the value-added by businesses in the European Union (Franco & Haase, 2009). Moreover, Haltiwanger & Miranda (2010) describe that young businesses (younger than 10 years) in the United States made a substantial contribution to job creation, with an average of over 20%, between 1994 and 2005. Naudé (2008), who did a literature review in order to
there is more than one evaluation site), harvests, also called crops or cuts, and all interactions between these sources, as well, the errors within the test (Smith et al., 2005). Because of the computational facility, the momentum methods, where the average squares of each component or source of variation are equated to their respective mathematical expectations to estimate the components of variance, are still widely used (Coelho and Barbin, 2006; Freitas et al., 2008). However, this approach leads to assumptions that should not be ignored, under threat of biased estimates of effects: normality, homogeneity of variances, and independence of sample errors, which are generally not observed in field trials, especially for perennial species (Onofri et al., 2010; Resende, 2002). In the absence of compliance with these requirements, artifacts such as adjustments in degrees of freedom and data transformation can be employed, but are not always effective solutions (Freitas et al., 2008; 2011). Onofri et al. (2010) state that, even if used properly and solving most of statistical problems in a simple way in agriculture, the application of ANOVA, although not technically incorrect, may be inefficient in cases of unbalanced trials in various environments or longitudinal data. For the longitudinal data case, a possibility of analysis would be by the average of subsamples or individually for each measurement, but feasible only in balanced cases and lead to loss of variation information; or analysis of the entire data set with distinction of two error levels for the experimental units allocated to the plots, which generally correspond to the genotypes, and to the observation units allocated to the subplots, generally for measures over time (Freitas et al., 2011; Onofri et al., 2010). In the case of measures over time, the use of the scheme in split-plot must necessarily satisfy the condition of sphericity, that is, the variances of the difference between pairs of errors are all equal (Freitas et al., 2008; Huynh and Feldt, 1970).
The quality of ambient air in residential districts is influenced mostly by air emissions of substances from nearby sources. The pollution results from the transport of these substances over large distances. Aerosols are small solid and liquid particles suspended in a gas phase. They originate as a result of processes on the surface of the earth and in the atmos- phere. They vary in shape, size, density and chemical composition . Primary particles in the air are the result of direct emissions from traf- fic, industry and combustion installations, agriculture, while secondary particles are the result of various physical-chemical processes in pollut- ed atmosphere . In addition to the anthropogenic sources, a portion of dust particles is also a result of natural processes in the environment (volcano eruptions, forest fires, wind erosion, plants pollen, etc.). Parti- cles that arise from different processes have consequently a different chemical structure, shape and physical characteristics. Various factors influence the elevated concentration of pollutants, such as climatic characteristics, meteorological phenomena, physical-chemical process- es of transformation of substances in the air and the topographical structure of the area .