Proses Literasi Matematis Dikaji dari Content Space And Shape dalam Materi Geometri di SMA

The purpose of this research is to explain the mathematics literacy process reviewed from the content space and shape on the geometry subject at the tenth grade of MIA 1 students of SMAN 6 Pontianak in academic year 2015/2016. The research method used is descriptive research with case study. The subject of this research is six students. The result of analyzing data indicated that mathematics literacy process in communication aspect, the students are not disposed in supposing their thought result and disturb when represent the step of solution correctly. In representation aspect, most of the students can present the contextual problem in the form of picture. In reasoning and argumentation aspects, the students are able in giving the logical expression which is completed by reason and picture until getting the reasonable conclusion. In problem solving strategy planning aspect, the students still face difficulty in solving the problems which needs procedur solution and planning solution, not only by using the formula

Model Independent Tests of Skyrmions and Their Holographic Cousins

We describe a new exact relation for large $N_c$ QCD for the long-distance behavior of baryon form factors in the chiral limit. This model-independent relation is used to test the consistency of the structure of several baryon models. All 4D semiclassical chiral soliton models satisfy the relation, as does the Pomarol-Wulzer holographic model of baryons as 5D Skyrmions. However, remarkably, we find that the holographic model treating baryons as instantons in the Sakai-Sugimoto model does not satisfy the relation.Comment: v2. Added references, corrected typo

Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.

Background and purposeChest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose-volume constraints.Materials and methodsTwenty-five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain. Decision tree modeling was used to determine chest wall syndrome (CWS) thresholds for individual features. Significant features were determined using independent multivariate methods. These methods incorporate out-of-bag estimation using Random forests (RF) and bootstrapping (100 iterations) using decision trees.ResultsUnivariate analysis identified rib dose to 1 cc < 4000 cGy (P = 0.01), chest wall dose to 30 cc < 1900 cGy (P = 0.035), rib Dmax < 5100 cGy (P = 0.05) and lung dose to 1000 cc < 70 cGy (P = 0.039) to be statistically significant thresholds for avoiding CWS. Subsequent multivariate analysis confirmed the importance of rib dose to 1 cc, chest wall dose to 30 cc, and rib Dmax. Using learning-curve experiments, the dataset proved to be self-consistent and provides a realistic model for CWS analysis.ConclusionsUsing machine learning algorithms in this first of its kind study, we identify robust features and cutoffs predictive for the rare clinical event of CWS. Additional data in planned subsequent multicenter studies will help increase the accuracy of multivariate analysis

A relative-error inertial-relaxed inexact projective splitting algorithm

For solving structured monotone inclusion problems involving the sum of finitely many maximal monotone operators, we propose and study a relative-error inertial-relaxed inexact projective splitting algorithm. The proposed algorithm benefits from a combination of inertial and relaxation effects, which are both controlled by parameters within a certain range. We propose sufficient conditions on these parameters and study the interplay between them in order to guarantee weak convergence of sequences generated by our algorithm. Additionally, the proposed algorithm also benefits from inexact subproblem solution within a relative-error criterion. Simple numerical experiments on LASSO problems indicate some improvement when compared with previous (noninertial and exact) versions of projective splitting

Arbovirus emergence in the temperate city of Córdoba, Argentina, 2009-2018

The distribution of arbovirus disease transmission is expanding from the tropics and subtropics into temperate regions worldwide. The temperate city of Córdoba, Argentina has been experiencing the emergence of dengue virus, transmitted by the mosquito Aedes aegypti, since 2009, when autochthonous transmission of the virus was first recorded in the city. The aim of this work is to characterize the emergence of dengue and related arboviruses (Zika and chikungunya) in Córdoba since 2009. Herein, we present a data set with all known information about transmission of dengue, Zika, and chikungunya viruses in Córdoba, Argentina from 2009-2018, including what information is known of dengue virus (DENV) serotypes in circulation and origins of imported cases. The data presented in this work will assist researchers in investigating drivers of arbovirus emergence and transmission in Córdoba, Argentina and contribute to a better understanding of the global problem of the expanding distribution of arbovirus disease transmission.Fil: Robert, Michael A.. University Of The Sciences; Estados UnidosFil: Tinunin, Daniela T.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; ArgentinaFil: Benitez, Elisabet Marina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; ArgentinaFil: Ludueña Almeida, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; ArgentinaFil: Romero, Moory. State University of New York; Estados UnidosFil: Stewart-Ibarra, Anna M.. State University of New York; Estados UnidosFil: Estallo, Elizabet Lilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; Argentin

Inference via Wild Bootstrap and Multiple Imputation under Fine-Gray Models with Incomplete Data

Fine-Gray models specify the subdistribution hazards for one out of multiple competing risks to be proportional. The estimators of parameters and cumulative incidence functions under Fine-Gray models have a simpler structure when data are censoring-complete than when they are more generally incomplete. This paper considers the case of incomplete data but it exploits the above-mentioned simpler estimator structure for which there exists a wild bootstrap approach for inferential purposes. The present idea is to link the methodology under censoring-completeness with the more general right-censoring regime with the help of multiple imputation. In a simulation study, this approach is compared to the estimation procedure proposed in the original paper by Fine and Gray when it is combined with a bootstrap approach. An application to a data set about hospital-acquired infections illustrates the method.Comment: 32 pages, 2 figures, 1 tabl