Need For Extensive Knowledge Research Articles

QUARTERLY VARIATION OF TROPOSPHERIC SURFACE REFRACTIVITY IN ILORIN, NIGERIA

To construct a radio communication system, there is need for extensive knowledge of the refractivity and its variations. This work aims to determine the change in season with tropospheric surface refractivity of Ilorin, Kwara, Nigeria. The refractivity for both dry (1st quarter) and rainy (3rd quarter) seasons for 2000 - 2002 have been calculated using the meteorological data obtained from the Nigerian Meteorological Agency, Ilorin. The result shows that there is a variation between tropospheric surface refractivity during the dry and rainy seasons. High refractivity during the rainy season than in dry season is observed, mainly due to higher amount of water content in the atmosphere (troposphere) in the rainy season.

Variabilities in global DNA methylation and β-sheet richness establish spectroscopic landscapes among subtypes of pancreatic cancer

PurposeKnowledge about pancreatic cancer (PC) biology has been growing rapidly in recent decades. Nevertheless, the survival of PC patients has not greatly improved. The development of a novel methodology suitable for deep investigation of the nature of PC tumors is of great importance. Molecular imaging techniques, such as Fourier transform infrared (FTIR) spectroscopy and Raman hyperspectral mapping (RHM) combined with advanced multivariate data analysis, were useful in studying the biochemical composition of PC tissue.MethodsHere, we evaluated the potential of molecular imaging in differentiating three groups of PC tumors, which originate from different precursor lesions. Specifically, we comprehensively investigated adenocarcinomas (ACs): conventional ductal AC, intraductal papillary mucinous carcinoma, and ampulla of Vater AC. FTIR microspectroscopy and RHM maps of 24 PC tissue slides were obtained, and comprehensive advanced statistical analyses, such as hierarchical clustering and nonnegative matrix factorization, were performed on a total of 211,355 Raman spectra. Additionally, we employed deep learning technology for the same task of PC subtyping to enable automation. The so-called convolutional neural network (CNN) was trained to recognize spectra specific to each PC group and then employed to generate CNN-prediction-based tissue maps. To identify the DNA methylation spectral markers, we used differently methylated, isolated DNA and compared the observed spectral differences with the results obtained from cellular nuclei regions of PC tissues.ResultsThe results showed significant differences among cancer tissues of the studied PC groups. The main findings are the varying content of β-sheet-rich proteins within the PC cells and alterations in the relative DNA methylation level. Our CNN model efficiently differentiated PC groups with 94% accuracy. The usage of CNN in the classification task did not require Raman spectral data preprocessing and eliminated the need for extensive knowledge of statistical methodologies.ConclusionsMolecular spectroscopy combined with CNN technology is a powerful tool for PC detection and subtyping. The molecular fingerprint of DNA methylation and β-sheet cytoplasmic proteins established by our results is different for the main PC groups and allowed the subtyping of pancreatic tumors, which can improve patient management and increase their survival. Our observations are of key importance in understanding the variability of PC and allow translation of the methodology into clinical practice by utilizing liquid biopsy testing.

Impact of the COVID-19 pandemic on individuals with nystagmus and an exploration of public assumptions about the condition: an electronic questionnaire study

PurposeNystagmus is a disorder characterized by uncontrolled, rhythmic oscillations of the eyes. It often causes reduced visual function beyond reduced visual acuity alone. There is a paucity of literature regarding the public understanding of nystagmus, and there are no published data on the impact of the COVID-19 pandemic on people living with the condition. This study explores the self-reported impact of the COVID-19 pandemic on those with nystagmus, and examines both public understanding of how nystagmus affects people who have it and the perceptions of public understanding by those with the condition and their carers.MethodsA qualitative questionnaire was designed following a stakeholder engagement process. This questionnaire was advertised via social media platforms and charity websites to achieve widespread recruitment. Data were collected between November and December 2020. Participants were divided into two groups based on their response to the question: “Do you, or anyone you know well, have nystagmus?”. Questions were posed to participants in a purpose-built, branching survey. The resulting data were analyzed using descriptive and inferential statistical methods.ResultsOne thousand six hundred forty-five respondents were recruited, of which 849 (51.6%) answered “Yes” to the initial filtering question. Analysis showed that, broadly, public understanding of nystagmus differs from the perception of it by those with nystagmus and their carers, that the COVID-19 pandemic has had a significant impact on those with nystagmus, and that respondents who have met someone with nystagmus, even briefly, tend to have a greater understanding of the impact of the condition.ConclusionThis study highlights the lack of public awareness regarding nystagmus and suggests opportunities to increase the awareness of nystagmus without the need for extensive knowledge of the condition. The COVID-19 pandemic has posed additional difficulties for those living with nystagmus, which is likely to be comparable among those with similar ocular disorders.

Improving performance with hybrid feature selection and ensemble machine learning techniques for code smell detection

Maintaining large and complex software is a significant task in IT industry. One reason for that is the development of code smells which are design flaws that lead to future bugs and errors. Code smells can be treated with regular refactoring, and their detection is the first step in the software maintenance process. Detecting code smells with machine learning algorithms eliminate the need of extensive knowledge required regarding properties of code smell and threshold values. Ensemble machine learning algorithms use a combination of several same or different classifiers to further aid the performance and reduces the variance. In our study, three hybrid feature selection techniques with ensemble machine learning algorithms are employed to improve the performance in detecting code smells. Seven machine learning classifiers with different kernel variations, along with three boosting designs, two stacking methods, and bagging were implemented. For feature selection, combination of filter-wrapper, filter-embedded, and wrapper-embedded methods have been executed. Performance measures for detecting four code smells are evaluated and are compared with the performance when feature selection is not employed. It is found out that performance measure after application of hybrid feature selection increased, accuracy by 21.43%, AUC value by 53.24%, and f-measure by 76.06%. Univariate ROC with Lasso is the best hybrid feature selection technique with 90.48% accuracy and 94.5% ROC AUC value. Random Forest and Logistic regression are the best performing machine learning classifiers. Data class is most detectable code smell. Stacking always gave better results when compared with individual classifiers.

Simplified structural design and LCA of reinforced concrete beams strengthening techniques

This work provides the Life Cycle Assessment (LCA) of four commonly used strengthening techniques of reinforced concrete beams. Firstly, it provides a simplified methodology to size the strengthening, overcoming the need of extensive knowledge in structures. Secondly, it provides the application of LCA to the selected techniques. The method improves the applicability of LCA to buildings, analyzes the environmental differences between techniques, and reveals the importance of the anchoring method as well as the enormous benefit in reusing building structures. Results obtained for conventional beams are displayed in tables ready to use in LCAs with broader boundary systems.

Data Collection Strategies and Measurement Tools for Assessing Academic and Therapeutic Outcomes in Recovery Schools

Accurate evaluation and documentation of the efficacy of recovery schools can be vital to the continuation and expansion of these beneficial resources. A very limited data set currently exists that examines the value of specific schools established to support adolescents and young adults in recovery; additional research is necessary. The following article outlines the methodology utilized in a current quasi-experimental study evaluating both academic and therapeutic outcomes of adolescents attending recovery high schools as compared to traditional (non-recovery-based) high schools. The developmental considerations in assessing adolescents in recovery and their parents is delineated in this article, which underscores the need for extensive knowledge of adolescent substance abuse and other mental health issues. In addition, sensitivity around privacy among adolescents, parents, schools, and health providers is highlighted, as well as the validity of assessment. Key assessment strategies, including protocol of recruitment and interviewing techniques, are also presented along with a list of parent and adolescent assessment instruments and their corresponding interpretive variables. Protocol recommendations for future research are also outlined.

Multiobjective constrained optimal synthesis of planar mechanisms using a new evolutionary algorithm

The paper deals with solution methods to multiobjective optimization problems. A searching procedure is developed, which applies genetic algorithms based on evolutionary techniques. The new method has proved to solve synthesis problems of planar mechanisms and has been used for testing a hand robot mechanism, showing that the solutions are accurate and valid for all cases. The extension of the method to other mechanism types has been outlined. The main advantages of the method are its simplicity of implementation and its fast convergence to optimal solutions, with no need of extensive knowledge of the searching space.

Visualization of sensory profiling data for performance monitoring

Simple graphical techniques can be utilized to investigate the performance of individual assessors and to detect individual differences among assessors in a sensory panel. By visualizing different type of information in a set of various plots the panel leader can extract relevant information without the need of extensive knowledge in statistics. The joint information content gained from the plots can then be used as a basis for selective training in order to improve performance of single assessors and the panel as a whole.

SACIMAGE—image processing software library

Abstract This article explains about SACIMAGE, the digital image processing software library available on various indigenously available hardware platforms ranging from PC to MINI and Super MINI computer systems. SACIMAGE software library contains 100 software packages to carry out various image processing functions such as data handling, image preprocessing, image enhancements, image classification for information extraction, image display functions, image hardcopy generation on various peripherals, cartography and mapping functions. Applications Development System (ADS) is a menudriven user-friendly environment provided to resource scientists to define their image processing jobs without the need for extensive knowledge of computer and operating environment. ADS also provides a facility for accounting the system performance and usage. SACIMAGE provides the tools for handling digital data from remote sensing platforms and other sources made available on computer compatible tapes, cartridge tapes and floppies,