Adverse effects of clear aligner orthodontic treatment – a literary review

Background and aim: Orthodontic treatment with clear aligners has been increasingly popular since introduction in the late 1990 ́s. This literary review is aimed to study the adverse effects in connection to clear aligner treatment regarding white spot lesions, root resorption, periodontal status, pain and discomfort. Material and methods: Search in the PubMed, Cochrane Library and Embase databases gave 1144 initial titles, and after removal of duplicates and reviewing for exclusion criteria resulted in a final amount of 30 articles. Inclusion criteria were healthy patients with aligner treatment. Keywords were: clear aligner (CA), Invisalign, white spot lesions (WSL), root resorption (RR), periodontal status (PERIO), pain and discomfort (P&D). Endnote was used for organizing articles and excluding duplicates. Results: Clear aligner treatment was presented to generate significantly less lesions (compared to fixed appliances treatment (6.2 vs. 8.3 lesions/patient); p < 0.05)). However, CA lesions were larger in area but shallower. Prevalence of root-resorption was significantly lower in a CA group compared to a fixed appliance group (56% vs. 82%; p < 0.001). Periodontal pocket depth was found to be significantly less on average in CA patients compared to FA patients in 5/6 articles. Pain and discomfort levels were significantly lower among CA patients than FA patients during the first week after initiation of treatment. Conclusion: This literary review clearly indicates that clear aligner treatment has less adverse effects regarding white spot lesions, root resorption, periodontal risk factors, and pain and discomfort compared to conventional fixed appliance treatment

APPLICATION OF MACHINE LEARNING IN PREDICTING CHILDREN'S NUTRITIONAL STATUS WITH MULTIPLE LINEAR REGRESSION MODELS

Forecasting is an important part of making plans and making decisions that can predict future events. Forecasting techniques in this study used multiple linear regression. This study aims to predict the number of cases of child nutritional status in children in each region. The purpose of this study was to see the results of predicting the number of children's nutritional status in each region and to make it easier to predict children's nutrition. The research method includes the analysis of the system built and the design of machine learning applications using the Multiple Linear Regression method. Then the system built can help predict the nutritional status of children in Aceh quickly, precisely, and accurately. The data used is data on the nutritional status of children in 2018, 2019, and 2020. Based on the results of forecasting for 2021 based on data obtained in previous years, the predicted results of total nutritional status in 2021 are 449,0912126. The results of this study indicate that the linear regression method obtains the best model results by being able to predict the implementation of machine learning

Comparison of fish community structure on artificial reefs deployed at different depths on turkish Aegean sea coast

A profundidade da implantação de recifes artificiais é uma das questões mais importantes no planejamento de etapas e do futuro êxito. A maior parte dos estudos visando determinar a comunidade de peixes em volta de recifes artificiais foi realizada, principalmente , a profundidades de 10-25m no Mediterrâneo e Mar Egeu. Os objetivos deste estudo são determinar e comparar a estrutura da comunidade de peixes em volta de recifes artificiais que foram implantados a profundidades de 20, 30 e 40 m. A técnica de censo visual foi usada para determinar as espécies e obter uma estimativa do número e tamanho dos peixes. Não houve nenhuma diferença estatística (p&gt; 0.05) entre as profundidades, na média da biomassa de peixes e no número de indivíduos. Entretanto, a média do número de espécies foi significativamente maior nos 20 m em comparação com as profundidades de 30 m e de 40 m (pDeployment depth of artificial reefs is one of the most important issue in planning stage and future success. Most of the studies aimed at determination of fish community around artificial reefs were conducted mainly 10-25m depths in Mediterranean and Aegean Sea. The goals of this study are determine and compare of fish community structure around artificial reefs which deployed 20, 30 and 40 m depths. Underwater visual census technique was used to determine fish species, number of individual and size estimation. There was no statistical difference (p&gt;0.05) in mean fish biomass and number of individual between the depths. But mean species number was significantly greater on 20 m in comparison to 30 m and 40 m depths (

APPLICATION OF MACHINE LEARNING IN DETERMINING THE CLASSIFICATION OF CHILDREN'S NUTRITION WITH DECISION TREE

The problem of nutrition for children is a health problem that must be solved by the government. Malnutrition is a very important problem in the development of children, especially during the growth period. Lack of nutritional intake in children will have a negative impact on resistance to the virus. This will risk death caused by malnutrition. There is direct monitoring from the government, hospitals, and health offices in looking at the classification of nutrition in children in a system. This study aims to classify the nutritional status of children using a machine learning model, which then the final result can show the classification of nutritional vulnerability in each patient at the North Aceh Hospital. The stages of the research include the identification of theories about nutritional problems. Second, data collection is in the form of symptoms and diagnosis of disease classification in machine learning implementation. The third is to analyze the data using the research and development (R&amp;D) method according to the classification of children's nutrition. Finally, the implementation of the patient classification model with decision tree into machine learning The variables included in the system include JK(X1), U (X2), BB(X3), TB (X4), and BB (X3), which are the variables that have the most influence on malnutrition in children. The results of this study for testing weight 16, height 9.7, age 33 months, nutritional value 54,23772 which the program output results are normal. Patient Syafira Nisman, weight 9, height 72, age 21 months, suffered from malnutrition. The results of the research on the application of machine learning for the classification of malnutrition using the decision tree method make it easier for patients and hospitals to classify children's nutrition

PENGARUH MODEL SNOWBALL THROWING TERHADAP HASIL BELAJAR SISWA PADA MATERI SISTEM PERTAHANAN TUBUH DI SMA SWASTA MEDAN

Penelitian ini bertujuan untuk mengetahui pengaruh Model Pembelajaran Snowball Throwing terhadap hasil belajar siswa pada materi Sistem Pertahanan Tubuh. Penelitian ini dilaksanakan selama 2 bulan. Penelitian ini menggunakan metode eksperimen semu (Quasi experiment) dengan populasi penelitian seluruh siswa di kelas XI yang terdiri 2 kelas dengan jumlah 64 siswa dan sampel penelitian siswa kelas XI IPA1 yang terdiri dari 35 siswa, penentuan sampel dilakukan secara Random sampling. Instrumen dalam penelitian ini adalah tes hasil belajar berupa pre-test dan post-test. Hasil belajar siswa yang menggunakan model pembelajaran Snowball Throwing diperoleh nilai yang tuntas adalah sebanyak 29 siswa (82,86%) dan yang tidak tuntas sebanyak 6 siswa (17,14%) dengan nilai rata-rata 77,8 dan standar deviasi 9,06. Hasil uji normalitas diperoleh Lhitung&lt;Ltabel  atau 0,1269&lt;0,1497 dinyatakan bahwa data berdistribusi normal sedangkan uji homogenitas diperoleh Fhitung&lt;Ftabel = 1,04&lt;1,776 dinyatakan data mempunyai varians yang sama atau homogen. Hasil uji hipotesis menggunakan uji t diperoleh thitung&gt; ttabel atau 14,27&gt;1,69 dengan taraf kepercayaan 0,05 maka Ha diterima Ho ditolak, sehingga dinyatakan bahwa ada pengaruh yang signifikan penggunaan model pembelajaran Snowball Throwing terhadap hasil belajar siswa pada materi Sistem Pertahanan Tubuh di Kelas XI SMA Istiqlal Delitua Medan

Handling missing data in analyses of the UK women's cohort study

Missing values are a problem in large-scale surveys with extensive questionnaires. The analysis of the complete records may yield inferences substantially different from those that would be obtained had no data been missing. The aim of this dissertation is to critically examine ways of handling missing data in the UK Women Cohort Study (UKWCS). This is a large dataset with continuous, categorical and binary variables with missing values in almost every variable. A number of simple imputation techniques, as well as multiple imputation developed by Rubin (1987), and multiple imputation by chained equations using the Gibbs sampling (Van Buuren, 1999), were explored in a number of illustrative analyses associated with the UKWCS. Three approaches of handling missing dietary information on alcohol consumption were compared. The comparison shows that ignoring missingness by analysing only complete cases produces bias (lower means). Imputing an extreme value zero as is customary at present, underestimates the actual alcohol consumption, it also incorrectly increases the apparent precision of estimation (i. e. inappropriately small standard errors). A published study, Pollard et al, (2001) which based its conclusion on one third of the records was replicated after handing missing data by multiple imputation. Multiple imputation by chained equations, an iterative technique, which deals with missing values when every variable is incomplete, was applied. This method greatly improved the results by utilizing most of the information in the incomplete records. The method has the advantage that the algorithm intended for analysing the complete data is applied several times, without any alterations. The implications of missing data were also studied in a survival analysis, investigating the link between incidence of breast cancer and a number of prognostic factors. The thesis recommends multiple imputation for handling missing data, by which most of the information in the dataset is exploited, and helps in efficient inferences to be made from subsequent analyses

Monthly Streamflow Generation With Linear Models

189 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1977.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Adverse effects of clear aligner orthodontic treatment – a literary review

Background and aim: Orthodontic treatment with clear aligners has been increasingly popular since introduction in the late 1990 ́s. This literary review is aimed to study the adverse effects in connection to clear aligner treatment regarding white spot lesions, root resorption, periodontal status, pain and discomfort. Material and methods: Search in the PubMed, Cochrane Library and Embase databases gave 1144 initial titles, and after removal of duplicates and reviewing for exclusion criteria resulted in a final amount of 30 articles. Inclusion criteria were healthy patients with aligner treatment. Keywords were: clear aligner (CA), Invisalign, white spot lesions (WSL), root resorption (RR), periodontal status (PERIO), pain and discomfort (P&D). Endnote was used for organizing articles and excluding duplicates. Results: Clear aligner treatment was presented to generate significantly less lesions (compared to fixed appliances treatment (6.2 vs. 8.3 lesions/patient); p < 0.05)). However, CA lesions were larger in area but shallower. Prevalence of root-resorption was significantly lower in a CA group compared to a fixed appliance group (56% vs. 82%; p < 0.001). Periodontal pocket depth was found to be significantly less on average in CA patients compared to FA patients in 5/6 articles. Pain and discomfort levels were significantly lower among CA patients than FA patients during the first week after initiation of treatment. Conclusion: This literary review clearly indicates that clear aligner treatment has less adverse effects regarding white spot lesions, root resorption, periodontal risk factors, and pain and discomfort compared to conventional fixed appliance treatment

Revolusi belajar : optimalisasi kecerdasan melalui pembelajaran berbasis kecerdasan majemuk

160 hlm.: ilus.; 21 c