On 3D Minimal Massive Gravity

We study linearized equations of motion of the newly proposed three dimensional gravity, known as minimal massive gravity, using its metric formulation. We observe that the resultant linearized equations are exactly the same as that of TMG by making use of a redefinition of the parameters of the model. In particular the model admits logarithmic modes at the critical points. We also study several vacuum solutions of the model, specially at a certain limit where the contribution of Chern-Simons term vanishes.Comment: 15 pages, no figures, typos fixed, journal versio

On Complexity for Higher Derivative Gravities

Using "complexity=action" proposal we study complexity growth of certain gravitational theories containing higher derivative terms. These include critical gravity in diverse dimensions. One observes that the complexity growth for neutral black holes saturates the proposed bound when the results are written in terms of physical quantities of the model. We will also study effects of shock wave to the complexity growth where we find that the presence of massive spin-2 mode slows down the rate of growth.Comment: 18 pages, 3 figures, journal versio

Strength of a clay soil and soil-cement mixture with resin

This is the final version of the article. Available from European Geosciences Union via the DOI in this record.A series of laboratory experiments were carried out to investigate the effect of resin on the strength of a clay soil and soil-cement mixtures. One group of tests were carried out on samples of the clay soil that were prepared with different resin contents. Another group of tests were conducted on mixtures of soil-cement and soil-cement-resin with specified resin contents. The results show that adding more than 10% resin increases the strength of the soil, whereas at resin contents below 10% no significant effect was observed. The strengths of the samples of soil, soil- cement mixture and soil-cement-resin mixture increased with increasing percentages of cement and resin. The results also show that the increase in strength is a function of percentage of agents and curing time

PREDICTION OF STUNTING PREVALENCE IN EAST JAVA PROVINCE WITH RANDOM FOREST ALGORITHM

Stunting or cases of failure to thrive in toddlers is one of the most serious health problems faced by the people of Indonesia. Based on data from the Ministry of Health and the Central Statistics Agency, East Java Province has a stunting prevalence value of 26.8% which is categorized as a high prevalence value according to the standards of the World Health Organization (WHO). Random forest is one of the machine learning algorithms in the field of artificial intelligence that can learn patterns from labeled data so that it can be used as a method for predicting or forecasting data. This approach is considered very suitable to be used in predicting the value of stunting prevalence because stunting prevalence data is usually accompanied by other data in the health sector according to survey results. Previous studies on the prediction of stunting prevalence used secondary data sourced from one survey only. Therefore, this study is one of the efforts to contribute in providing solutions for the stunting problem in East Java Province by combining several data from different surveys in the same year. The results of this study show that from 20 factor candidates for predicting stunting prevalence value, only 12 factors are suspected to be causative factors based on their correlation value. However, the prediction results obtained using the random forest algorithm in this study, with data consisting of 12 features and a dataset consisting of only 38 data, have results with error values of 1.02 in MAE and 1.64 in MSE that are not better than multi-linear regression which can produce smaller error values of 0.93 in MAE and 1.34 in MSE

An isolated mass in the palm, starting manifestation of sarcoidosis

Sarcoidosis is a systemic disease that is characterized with noncaseating granulomatous nodules which present in multiple organs specially lungs (90). Incidence of masses due to Sarcoidosis in upper extremity is low and most cases present in association with involvement of pulmonary hilary lymph nodes. In this article we present a rare case of Sarcoidosis which presented as a single soft tissue mass in hand without osseous or pulmonary hillary lymph node involvement. Incidence of involvement of musculoskeletal system is 1-5 , mostly it occurs in small bones in hands and feet. In most cases involvement of soft tissue in extremities is accompanied with bone lesions. Those cases of soft tissue involvement are generally coincide with pulmonary lymph nodules. To the authors' knowledge, this is the first case of Sarcoidosis that presents without spreading in bones or pulmonary hilar lymph nodes. © 2016 BY THE ARCHIVES OF BONE AND JOINT SURGERY

Holographic renormalization of 3D minimal massive gravity

We study holographic renormalization of 3D minimal massive gravity using the Chern-Simons-like formulation of the model. We explicitly present Gibbons- Hawking term as well as all counterterms needed to make the action finite in terms of dreibein and spin-connection. This can be used to find correlation functions of stress tensor of holographic dual field theory.Comment: 26 pages, no figures, typos fixed, Ref. adde

Perbandingan Metode Supervised Machine Learning untuk Prediksi Prevalensi Stunting di Provisi Jawa Timur

Stunting atau kasus balita kerdil/pendek adalah salah satu masalah di bidang kesehatan yang saat ini sedang dihadapi oleh masyarakat Indonesia. Provinsi Jawa Timur memiliki nilai prevalensi stunting sebesar 26,8% berdasarkan integrasi data Kementerian Kesehatan dan Badan Pusat Statistik. Nilai tersebut masih tergolong tinggi karena standar minimal yang ditetapkan oleh World Health Organization (WHO) adalah sebesar 20%. Oleh karena itu, penelitian ini bertujuan untuk memberikan kontribusi dalam penyelesaian permasalahan stunting di Provinsi Jawa Timur dengan cara menganalisis faktor-faktor yang diprediksi bisa memengaruhi tingkat prevalensi stunting berdasarkan data sekunder hasil survei dari beberapa lembaga resmi dan terpercaya di bidang kesehatan yang telah dipublikasikan. Supervised machine learning merupakan pendekatan dalam pembuatan kecerdasan buatan (artificial intelligence) yang menggunakan data-data berlabel sebagai data latihnya. Pendekatan ini dirasa sangat sesuai digunakan dalam prediksi nilai prevalensi stunting pada suatu wilayah berdasarkan data-data lain yang relevan.  Penelitian-penelitian sebelumnya tentang prediksi prevalensi stunting rata-rata hanya menggunakan salah satu metode supervised machine learning saja dan data sekunder yang digunakan hanya bersumber dari salah satu sumber survei saja. Hasil penelitian menunjukkan bahwa faktor-faktor penyebab yang memiliki korelasi tinggi terhadap nilai prevalensi stunting bukan hanya Berat Badan Lahir Rendah (BBLR) saja, namun juga Indeks Pembangunan Manusia, sanitasi, dan Indeks Penduduk Miskin. Selain itu, beberapa metode dalam supervised machine learning juga dibandingkan yaitu, linier regression, support vector regression, dan random forest regression.Metode support vector regression dalam penelitian ini memiliki nilai galat yang lebih rendah yaitu 0,91 untuk MAE dan 1,30 untuk MSE.AbstractStunting or the case of stunted/short toddlers is one of the problems in the health sector that is currently being faced by the people of Indonesia. East Java Province has a stunting prevalence value of 26.8% based on data integration from the Ministry of Health and the Central Statistics Agency. This value is still relatively high because the minimum standard set by the World Health Organization (WHO) is 20%. Therefore, this study aims to contribute to solving the stunting problem in East Java Province by analyzing the factors that are predicted to affect the stunting prevalence rate based on published secondary data from surveys from several official and trusted institutions in the health sector. Supervised machine learning is an approach in making artificial intelligence that uses labeled data as training data. This approach is considered very suitable to be used in predicting the value of stunting prevalence in an area based on other relevant data. Previous studies on predicting the prevalence of stunting on average only used one supervised machine learning method and the secondary data used was only sourced from one survey source. The results showed that the causative factors that have a high correlation to the prevalence of stunting are not only low birth weight (BBLR), but also the Human Development Index, sanitation, and the Poor Population Index. In addition, several methods in supervised machine learning are also compared, namely, linear regression, support vector regression, and random forest regression. The support vector regression method in this study has a lower error value, namely 0.91 for MAE and 1.30 for MSE