26,973 research outputs found

    The Correlation between Leukocytes and Bacterial Number from Urine of Type 2 Diabetes Mellitus Patients Using Urine Analyzer

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    Diabetes Mellitus (DM) is a chronic metabolic disorder. DM can occur if the pancreas does not produce enough insulin or the insulin that is produced isn’t effectively used. Patients with diabetes mellitus have a high risk for chronic microvascular complications, including infection. This research aimed to determine the correlation between leukocytes and bacterial number from urine of patients with Type 2 Diabetes Mellitus at the X Hospital Blitar. This research used a cross sectional method, with 47 urine samples of Type 2 Diabetes Mellitus patients by purposive sampling, were tested with urine analyzer. The result showed that the number of high leukocytes was 13 (28%) respondents and normal leukocytes was 34 (72%) respondents. While the number of bacteria from respondents  was normal 21 (45%) and high number was 26 (55%). Based on the Spearman-rho correlation test on SPSS, it can be concluded that there was a significant relationship between leukocytes with bacterial number of urine from Type 2 Diabetes Mellitus patients, with sig = 0,000 (0,05) and the correlation coefficient = 0,515

    An Experimental Study on Sentiment Classification of Moroccan dialect texts in the web

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    With the rapid growth of the use of social media websites, obtaining the users' feedback automatically became a crucial task to evaluate their tendencies and behaviors online. Despite this great availability of information, and the increasing number of Arabic users only few research has managed to treat Arabic dialects. The purpose of this paper is to study the opinion and emotion expressed in real Moroccan texts precisely in the YouTube comments using some well-known and commonly used methods for sentiment analysis. In this paper, we present our work of Moroccan dialect comments classification using Machine Learning (ML) models and based on our collected and manually annotated YouTube Moroccan dialect dataset. By employing many text preprocessing and data representation techniques we aim to compare our classification results utilizing the most commonly used supervised classifiers: k-nearest neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and deep learning (DL) classifiers such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LTSM). Experiments were performed using both raw and preprocessed data to show the importance of the preprocessing. In fact, the experimental results prove that DL models have a better performance for Moroccan Dialect than classical approaches and we achieved an accuracy of 90%.Comment: 13 pages, 5 tables, 2 figure

    EFEKTIFITAS ACTIVE ASSISTIVE RANGE OF MOTION TERHADAP PENURUNAN KGD PADA PASIEN DIABETES MELLITUS TIPE II

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    Diabetes Mellitus is a group of metabolic diseases that occur due to abnormalities in insulin secretion. Diabetes is not controlled and over time causes serious damage, especially to the nerves and blood vessels. This study aims to determine the effectiveness of Active Assistive Range of Motion in reducing KGD in patients with Type II DM. This research is a quantitative study with a quasi-experimental design with one group pretest and posttest design. The population in this study was all elderly people at the Sentosa Baru Health Center, totaling 167 people. The sampling technique was purposive sampling and analysis using the T-test. The results of this study show that the Active Assistive Range of Motion is effective in reducing KGD in type II DM at the UPTD Puskesmas Sentosa Baru with the results showing that the highest KGD before the intervention was 291 mg/dL, the lowest value was 220 mg/dL, after the intervention the highest value was 204 mg. /dL, the lowest value is 190 mg/dL. the results of bivariate analysis using the T-test obtained a p-value of 0.001 or p <0.05. Expected to be information for health workers, especially the implementation of Active Assistive Range of Motion as an effort to reduce KGD in Type II DM patients

    TERAPI RELAKSASI OTOT PROGRESIF DALAM KADAR GULA DARAH PENDERITA DIABETES MELITUS: LITERATURE REVIEW

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    Aktivitas fisik akan membuat metabolisme tubuh bekerja lebih optimal, akibatnya kadar glukosa darah akan terkontrol. Oleh karena itu diperlukan penanganan yang holistik. Progressive Muscle Relaxation (PMR) adalah jenis latihan tersebut. Tujuan dari penelitian ini adalah untuk mereview pengaruh terapi Relaksasi Otot Progresif terhadap penurunan kadar gula darah pada penderita Diabetes Melitus. Penelitian ini menggunakan metode literature review dengan PICO sebagai metode pencarian artikel-artikel di database. Database yang diakses PubMed, dan alat pencarian artikel yaitu Google Scholar. Kata kunci yang digunakan Diabetes Melitus, Relaksasi Otot progresif, Gula Darah. Total artikel yang didapatkan menggunakan database PubMed (6) dan mesin pencarian Google Scholar (215) dari tahun 2012-2022. Terdapat 6 artikel (PubMed 1 artikel dan Google Scholar 5 artikel) masuk kedalam literature review ini. Dari literature review didapatkan hasil mengenai durasi, frekuensi dan adanya pengaruh terhadap kadar gula darah pada penderita Diabetes Melitus. Hasil literature review penelitian ini dapat dijadikan sebagai data dasar untuk pemberian informasi atau menyediakan materi edukasi untuk pasien-pasien yang mengalami Diabetes Melitus dalam menurunkan kadar gula darah, serta untuk peneliti selanjutnya dapat melihat tentang durasi dan frekuensi dari pemberian tehnik relaksasi otot progresif dalam membantu penurunan kadar gula darah

    On the ergodic theory of the real Rel foliation

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    Let H\mathcal{H} be a stratum of translation surfaces with at least two singularities, let mHm_{\mathcal{H}} denote the Masur-Veech measure on H\mathcal{H}, and let Z0Z_0 be a flow on (H,mH)(\mathcal{H}, m_{\mathcal{H}}) obtained by integrating a Rel vector field. We prove that Z0Z_0 is mixing of all orders, and in particular is ergodic. We also characterize the ergodicity of flows defined by Rel vector field, for more general spaces (L,mL)(\mathcal{L}, m_{\mathcal{L}}), where LH\mathcal{L} \subset \mathcal{H} is an orbit-closure for the action of G=SL2(R)G = \mathrm{SL}_2(\mathbb{R}) (i.e., an affine invariant subvariety) and mLm_{\mathcal{L}} is the natural measure. Our results are conditional on a forthcoming measure classification result of Brown, Eskin, Filip and Rodriguez-Hertz.We also prove that the entropy of the action of Z0Z_0 on (\mathcal{L}, m_{\mathcal{L}) has zero entropy.Comment: This version contains a new result about entropy. Also minor changes were made to improve the presentation, and the title was change

    Trisecting a 4-dimensional book into three chapters

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    We describe an algorithm that takes as input an open book decomposition of a closed oriented 4-manifold and outputs an explicit trisection diagram of that 4-manifold. Moreover, a slight variation of this algorithm also works for open books on manifolds with non-empty boundary and for 3-manifold bundles over the circle. We apply this algorithm to several simple open books, demonstrate that it is compatible with various topological constructions, and argue that it generalizes and unifies several previously known constructions.Comment: 29 pages, 15 figure

    Generalized Weak Supervision for Neural Information Retrieval

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    Neural ranking models (NRMs) have demonstrated effective performance in several information retrieval (IR) tasks. However, training NRMs often requires large-scale training data, which is difficult and expensive to obtain. To address this issue, one can train NRMs via weak supervision, where a large dataset is automatically generated using an existing ranking model (called the weak labeler) for training NRMs. Weakly supervised NRMs can generalize from the observed data and significantly outperform the weak labeler. This paper generalizes this idea through an iterative re-labeling process, demonstrating that weakly supervised models can iteratively play the role of weak labeler and significantly improve ranking performance without using manually labeled data. The proposed Generalized Weak Supervision (GWS) solution is generic and orthogonal to the ranking model architecture. This paper offers four implementations of GWS: self-labeling, cross-labeling, joint cross- and self-labeling, and greedy multi-labeling. GWS also benefits from a query importance weighting mechanism based on query performance prediction methods to reduce noise in the generated training data. We further draw a theoretical connection between self-labeling and Expectation-Maximization. Our experiments on two passage retrieval benchmarks suggest that all implementations of GWS lead to substantial improvements compared to weak supervision in all cases

    Analysis of Single-Pilot Intention Modeling in Commercial Aviation

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    With the continual enhancement of the onboard avionics, the minimum flight crew has been downsized from five to two-person crew mode, and reduced crew operation has drawn extensive attention from aviation experts. Single-pilot operation (SPO) mode warrants careful account and research. This study investigated the intention modeling of commercial aviation single pilot based on the bidirectional long short-term memory (BiLSTM), mining the intention tendency of pilots’ behavior through artificial intelligence technology. This was done to avoid safety hazards caused by different intents and inconsistent operations of the single pilot and the cockpit automation system. The classification task of a single pilot’s behavior is the core of intention recognition. Various operation items contribute differently to the classification. To construct the interaction dataset and encode it into time series features, a single-pilot experiment is specifically performed, wherein the experience of an expert is summarized into single-pilot intent labels. The deep information in the feature vector of a single-pilot operation item is captured by the BiLSTM network, and the neural weight is adaptively assigned by the training mechanism. The operation sequence with the feature data is finally loaded into the softmax layer for intention classification. The proposed method is evaluated against long short-term memory (LSTM), term frequency-inverse document frequency (TF-IDF), convolutional neural network (CNN), Naive Bayesian (NB), and distributed representation’s intention modeling techniques. Because the proposed methods have higher F1 scores, the model can effectively share real-time information about the single-pilot intention with the cockpit automation system
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