The pore system in carbonates is complicated because of the associated biological and chemical activity. Secondary porosity, on the other hand, is the result of chemical reactions that occur during diagenetic processes. A thorough understanding of the carbonate pore system is essential to hydrocarbon prospecting. Porosity classification schemes are currently limited to accurately forecast the petrophysical parameters of different reservoirs with various origins and depositional environments. Although rock classification offers a way to describe lithofacies, it has no impact on the application of the poro-perm correlation. An outstanding example of pore complexity (both in terms of type and origin) may be found in the Central Luconia carbonate system (Malaysia), which has been altered by diagenetic processes. Using transmitted light microscopy, 32 high-resolution pictures were collected of each thin segment for quantitative examination. An FESEM picture and a petrographic study of thin sections were used to quantify the grains, matrix, cement, and macroporosity (pore types). Microporosity was determined by subtracting macroporosity from total porosity using a point-counting technique. Moldic porosity (macroporosity) was shown to be the predominant type of porosity in thin sections, whereas microporosity seems to account for 40 to 50% of the overall porosity. Carbonates from the Miocene have been shown to possess a substantial quantity of microporosity, making hydrocarbon estimate and production much more difficult. It might lead to a higher level of uncertainty in the estimation of hydrocarbon reserves if ignored. Existing porosity classifications cannot be used to better understand the poro-perm correlation because of the wide range of geological characteristics. However, by considering pore types and pore structures, which may be separated into macro- and microporosity, the classification can be enhanced. Microporosity identification and classification investigations have become a key problem in limestone reservoirs across the globe.

High dimensional data are commonly encountered in various scientific fields and pose great challenges to modern statistical analysis. To address this issue different penalized regression procedures have been introduced in the litrature, but these methods cannot cope with the problem of outliers and leverage points in the heavy tailed high dimensional data. For this purppose, a new Robust Adaptive Lasso (RAL) method is proposed which is based on pearson residuals weighting scheme. The weight function determines the compatibility of each observations and downweight it if they are inconsistent with the assumed model. It is observed that RAL estimator can correctly select the covariates with non-zero coefficients and can estimate parameters, simultaneously, not only in the presence of influential observations, but also in the presence of high multicolliearity. We also discuss the model selection oracle property and the asymptotic normality of the RAL. Simulations findings and real data examples also demonstrate the better performance of the proposed penalized regression approach.

This article introduces a quantile penalized regression technique for variable selection and estimation of conditional quantiles of counts in sparse high-dimensional models. The direct estimation and variable selection of the quantile regression is not feasible due to the discreteness of the count data and nondifferentiability of the objective function, therefore, some smoothness must be artificially imposed on the problem. To achieve the necessary smoothness, we use the Jittering process by adding a uniformly distributed noise to the response count variable. The proposed method is compared with the existing penalized regression methods in terms of prediction accuracy and variable selection. We compare the proposed approach in zero-inflated count data regression models and in the presence of outliers. The performance and implementation of the proposed method are illustrated by detailed simulation studies and real data applications.

Saat ini praktik perkawinan usia dini merupakan hal yang sangat memprihatinkan. Hasil observasi yang kami lakukan diketahui bahwa sebagian besar anak-anak yang putus sekolah khususnya wanita di desa Hiyang Bana menikah di usia dini, mayoritas dilakukan setelah mereka lulus SMP. Hal Tersebut menjadi latar belakang sehingga kami melakukan pengabdian masyarakat di desa Hiyang Bana. Fokus pengabdian ini berupa edukasi mengenai dampak perkawinan usia dini di desa Hiyang Bana, kecamatan Tasik Payawan. Metode kegiatan yang digunakan dalam kegiatan ini berupa ceramah, diskusi dan tanya jawab. Kegiatan dimulai dengan edukasi mengenai dampak dari pernikahan dini dengan narasumber dari mahasiswa KKN, Bidan yang ditugaskan di desa Hiyang Bana dan salah satu narasumber dari dewan Guru SMP Negeri 3 Tasik Payawan. Kegiatan edukdasi dampak perkawinan usia dini dapat disimpulkan berhasil, dapat dilihat dari presentase pemahaman peserta terhadap materi yang disampaikan ketika dilakukan diskusi dan tanya jawab, dimana 85% peserta sosialisasi memahami apa saja faktor penyebab terjadinya perkawinan usia dini, apa saja dampaknya, bagaimana dasar hukumnya dan bagaimana mencegah perkawinan usia dini. Lebih dari 90% peserta berkemauan untuk terus melanjutkan pendidikan ke jenjang yang lebih tinggi guna mencegah terjadinya perkawinan usia dini. Kegiatan edukasi seperti ini dapat diikuti dan terus dilakukan oleh pihak-pihak terkait kepada para siswa agar dapat memiliki pola pikir yang lebih luas sehingga pernikahan dini dapat diminimalisir.