Functional data dimensionality reduction for machine learning

Monimuuttuja-analyysissä korkeadimensioinen informaatio yleistyy jatkuvasti. Korkean dimension seurauksena laskenta-ajat kasvavat ja ongelmia aiheutuu myös nk. dimensionalisuuden kirouksen (curse of dimensionality) seurauksena. Tämä diplomityö koskee funktionaliseen data analyysiin perustuvaa dimensionalisuuden pienetämismenetelmää. Tässä menetelmässä korkeadimensioinen informaatio projisoidaan funktioavaruuteen jossa se voidaan kuvata yksinkertasemmassa muodossa. Funktioavaruus määritellään Gaussisten kantafunktioiden avulla, jotka on sovitetty kyseessä olevaan ongelmaan mahdollisimman hyvin. Esitetyttyä menetelmää sovelletaan kemometriaan ja aikasarjaennustukseen. Regressioon käytetään molemmissa tapauksissa pienimmän neliösumman tukivektorikonetta (Least-Squares Support Vector Machine). Koetulokset osoittavat, että dimensionalisuutta voidaan pienentää merkittävästi. Lisäksi saavutettu ennustustarkkuus on parempi tai vähintään samantasoinen verrattuna muihin yleisesti käytössä oleviin menetelmiin.High dimensional data are becoming more and more common in the field of multivariate data analysis. However, the high dimensionality is problematic due to increasing computational costs and to the curse of dimensionality. This thesis concerns dimensionality reduction method that is based on Functional Data Analysis. High dimensional data are projected on a function space where it can be expressed in more compact form. The functions space is defined by a set of Gaussian basis functions that are specially adjusted to suit the problem at hand. The methodology is tested in two applications, chemometrics and time series prediction, using Least-Squares Support Vector Machines for regression. The experiential results indicate that data dimension can be dramatically reduced. And what is more, the prediction accuracy is clearly better or at least equivalent compared to other commonly used methods

Plume spreading test case

We present a test case of river plume spreading to evaluate numerical methods used in coastal ocean modeling. It includes an estuary-shelf system whose dynamics combine nonlinear flow regimes with sharp frontal boundaries and linear regimes with cross-shore geostrophic balance. This system is highly sensitive to physical or numerical dissipation and mixing. The main characteristics of the plume dynamics are predicted analytically, but are difficult to reproduce numerically because of numerical mixing present in the models. Our test case reveals the level of numerical mixing as well as the ability of models to reproduce nonlinear processes and frontal zone dynamics. We present numerical solutions for Thetis and FESOM-C models on an unstructured triangular mesh, as well as ones for GETM and FESOM-C models on a quadrilateral mesh

HUBUNGAN TINGKAT PENGETAHUAN, SIKAP, DAN MOTIVASI DENGAN PERILAKU CUCI TANGAN PAKAI SABUN (CTPS) PADA SISWA SEKOLAH DASAR NEGERI TRIDADI, SLEMAN, DIY

Latar Belakang: Cuci tangan pakai sabun merupakan salah satu tindakan sanitasi dengan membersihkan tangan dan jari-jemari menggunakan air dan sabun untuk menjadi bersih serta dapat mencegah terjadinya penyakit. Cuci tangan pakai sabun merupakan indikator dari program Perilaku Hidup Bersih dan Sehat (PHBS) di sekolah. Kebiasaan cuci tangan penting untuk diajarkan sejak dini karena anak-anak merupakan calon agen perubahan untuk lingkungan sekitarnya. Salah satu faktor yang mempengaruhi terbentuknya perilaku cuci tangan adalah pengetahuan, sikap, motivasi. Oleh karena itu, penelitian ini bertujuan untuk mengetahui hubungan antara tingkat pengetahuan, sikap, dan motivasi dengan perilaku cuci tangan pakai sabun (CTPS) pada siswa SDN Tridadi, Sleman, DIY. Metode: Jenis penelitian kuantitatif menggunakan metode analitik observasional dengan pendekatan cross sectional. Sampel dalam penelitian ini adalah siswa kelas 4 dan 5 SDN Tridadi sebanyak 46 responden menggunakan teknik total sampling. Instrumen penelitian menggunakan kuesioner. Analisi data menggunakan analisis univariat dan bivariat yaitu uji chi square. Hasil: Hasil penelitian menunjukkan 65,2% siswa memiliki pengetahuan tinggi 60,9% siswa memiliki sikap tinggi. 56,5% siswa memiliki motivasi tinggi. Serta 54,3% siswa memiliki perilaku cuci tangan pakai sabun baik. Hasil uji statistik dengan analisis Chi Square menunjukkan ada hubungan antara tingkat pengetahuan (P= 0,047), sikap (P= 0,001), dan motivasi (P= 0,044) dengan perilaku CTPS pada siswa SDN Tridadi, Sleman, DIY. Kesimpulan: Berdasarkan hasil penelitian, didapatkan bahwa ada hubungan antara tingkat pengetahuan, sikap, dan motivasi dengan perilaku cuci tangan pakai sabun pada siswa SDN Tridadi, Sleman, DIY

PENERAPAN DATA MINING UNTUK ANALISIS DATA BENCANA MILIK BNPB MENGGUNAKAN ALGORITMA K-MEANS DAN LINEAR REGRESSION

Indonesia memiliki sejarah kejadian bencana alam yang cukup banyak, diantaranya adalah tsunami, gempa bumi, tanah longsor, kekeringan, banjir, letusan gunung berapi, dan sebagainya. Salah satu penyebab banyaknya potensi kejadian bencana alam di Indonesia adalah letak Indonesia yang berada di pertemuan lempeng – lempeng Eurasia, Indo-Australia dan Pasifik. Pertemuan lempeng dalam jangka panjang akan menghimpun energi yang suatu waktu akan lepas dan dapat menghasilkan bencana. Pengetahuan teknologi dan informasi pada saat ini sedang mengalami perkembangan yang pesat.Informasi tentang jumlah kejadian bencana alam dibutuhkan untuk penanggulangan bencana.Pengolahan data bencana alam yang umum dilakukan yaitu menggunakan teknik data mining, karena metode ini dianggap mampu menjadi solusi atas permasalahan penanggulangan bencana alam. Oleh karena itu, dalam penelitian ini membahas tentang pengelompokkan jumlah data bencana dan prediksi data bencana yang akan terjadi 5 tahun kedepan menggunakan teknik data mining. Algoritmadata mining yang digunakan adalah K-Means untuk clustering dan Linear Regression untuk prediksi data bencana.Kata kunci: Clustering, Data Bencana Alam, Data Mining, Linear Regression, WEK

Previous radiotherapy improves treatment responses and causes a trend toward longer time to progression among patients with immune checkpoint inhibitor-related adverse events

Background: Immune-related adverse events (irAEs) are frequently encountered by patients during immune checkpoint inhibitor (ICI) treatment and are associated with better treatment outcomes. The sequencing of radiotherapy (RT) and ICIs is widely used in current clinical practice, but its effect on survival has remained unclear. Methods: In a real-world multicenter study including 521 patients who received ICI treatment for metastatic or locally advanced cancer, RT schedules and timing, irAEs, time to progression, overall survival, and treatment responses were retrospectively reviewed. Results: Patients who received previous RT and developed irAE (RT +/AE +) had the best overall response rate (ORR 44.0%). The ORR was 40.1% in the RT −/AE + group, 26.7% in the RT −/AE − group and 18.3% in the RT + /AE − group (p < 0.001). There was a significantly longer time to progression (TTP) in the RT + /AE + group compared to the RT −/AE − and RT + /AE − groups (log rank p = 0.001 and p < 0.001, respectively), but the trend toward longer TTP in the RT + /AE + group did not reach statistical significance in pairwise comparison to that in the RT −/AE + group. Preceding RT timing and intent had no statistically significant effect on TTP. In a multivariate model, ECOG = 0 and occurrence of irAEs remained independent positive prognostic factors for TTP (HR 0.737; 95% CI 0.582–0.935; p = 0.012, and HR 0.620; 95% CI 0.499–0.769; p < 0.001, respectively). Conclusions: Better ORR and a trend toward longer TTP were demonstrated for patients with RT preceding ICI treatment and development of irAEs, which suggests that RT may boost the therapeutic effect of immunotherapy in patients with metastatic cancers.Peer reviewe

Development of a baroclinic discontinuous Galerkin finite element model for estuarine and coastal flows

Numerical marine models have become indispensable in ocean sciences. Despite many developments over the past decades, modelling of the coastal ocean is still an area of active research. In recent years, unstructured mesh models have gained attention, as they can better represent the complex topography of coastal domains compared to standard models. The inherent flexibility of unstructured meshes is particularly useful in multi-scale applications, where a wide spectrum of spatial scales must be captured. This thesis deals with the development of discontinuous Galerkin finite element shallow water models, with focus on shelf sea-estuary-river network systems. Two key issues in coastal marine modelling are addressed: wetting-drying and three-dimensional modelling of buoyancy driven flows. Representing the periodic exposure and submerging of tidal flats is a complicated task for Eulerian models. Most wetting-drying techniques are model specific, and require explicit time integration, which can significantly increase the computational cost. A generic wetting-drying method for depth-averaged shallow water equations is presented, which is compatible with implicit time marching, thus improving the computational efficiency. The latter part of the thesis is devoted for the development of a three-dimensional baroclinic model. A discontinuous Galerkin finite element discretisation is presented, combined with an explicit time integration method. Slope limiters are used to ensure stability in strongly baroclinic flows. To account for vertical mixing, the model is coupled to an established turbulence closure model library. The model is validated with standard benchmarks and a Rhine river plume simulation in an idealised geometry.(FSA 3) -- UCL, 201