Pemelajaran Tata Bahasa Berbasis Teks Siswa Kelas V Sekolah Dasar di Kota Medan

Penelitian ini mencoba menjawab permasalahan yang dihadapi guru tentang pemelajaran tata bahasa Indonesia yang tidak terdapat pada buku pegangan siswa maupun guru. Di samping itu, siswa kesulitan dalam membedakan imbuhan di- dan kata depan di yang terdapat pada teks. Permasalahan yang dibahas dalam penelitian ini yaitu bagaimana pemelajaran tata bahasa berbasis teks siswa sekolah dasar di Kota Medan dan bagaimana hasil belajarnya. Metode penelitian yang digunakan yaitu metode kualitatif dan kuantitatif dengan sumber data proses kegiatan belajar mengajar yang diambil dengan cara observasi langsung dan hasil belajar siswa yang diperoleh dari hasil tes. Lokasi penelitian adalah dua sekolah yang berada di Kecamatan Medan Selayang dan Medan Johor dengan jumlah siswa sebanyak 60 orang. Pemelajaran tata bahasa berbasis teks siswa kelas V sekolah dasar di Kota Medan menciptakan sistem pemelajaran berfokus pada siswa. Siswa dituntut untuk mampu mengidentifikasi, mengklasifikasi, serta mampu menemukan permasalahan dan menjawab permasalahan dengan tuntunan guru. Guru dijadikan sebagai fasilitator dan motivator dalam kegiatan belajar mengajar. Adapun nilai rata-rata hasil kegitan belajar mengajar belajar dengan menerapan model pemelajaran tata bahasa berbasis teks dengan cara belajar siswa aktif berjumlah 77,75. Nilai 77,75 dikategorikan baik

Analysis of Unsteady Squeezing Flow Between Two Porous Plates With Variable Magnetic Field

Analysis will be made for the non-isothermal Newtonian fuid flow between two unsteady squeezing porous plates under the infuence of variable magnetic feld. The similarity transformations will be used to transform the partial differential equations into nonlinear coupled ordinary differential equations. The modeled nonlinear differential equations representing the flow behavior in the geometry under consideration will be investigated using analytical and numerical method. Comparison of the solutions will be made. Convergence of solution will also be discussed. Flow behavior under the infuence of non-dimensional parameters will be discussed with the help of graphical aids

Analysis of Physiochemical Parameters to Evaluate the Drinking Water Quality in the State of Perak, Malaysia

YesThe drinking water quality was investigated in suspected parts of Perak state, Malaysia, to ensure the continuous supply of clean and safe drinking water for the public health protection. In this regard, a detailed physical and chemical analysis of drinking water samples was carried out in different residential and commercial areas of the state. A number of parameters such as pH, turbidity, conductivity, total suspended solids (TSS), total dissolved solids (TDS), and heavy metals such as Cu, Zn, Mg, Fe, Cd, Pb, Cr, As, Hg, and Sn were analysed for each water sample collected during winter and summer periods. The obtained values of each parameter were compared with the standard values set by the World Health Organization (WHO) and local standards such as National Drinking Water Quality Standard (NDWQS). The values of each parameter were found to be within the safe limits set by the WHO and NDWQS. Overall, the water from all the locations was found to be safe as drinking water. However, it is also important to investigate other potential water contaminations such as chemicals and microbial and radiological materials for a longer period of time, including human body fluids, in order to assess the overall water quality of Perak state

Follicle size on day of trigger most likely to yield a mature oocyte

Funding: MRC, BBSRC and NIHR and supported by the NIHR/Wellcome Trust Imperial Clinical Research Facility and Imperial Biomedical Research Centre.Objective: To identify follicle sizes on the day of trigger most likely to yield a mature oocyte following hCG, GnRH agonist (GnRHa), or kisspeptin during IVF treatment. Design: Retrospective analysis to determine the size of follicles on day of trigger contributing most to the number of mature oocytes retrieved using generalized linear regression and random forest models applied to data from IVF cycles (2014–2017) in which either hCG, GnRHa, or kisspeptin trigger was used. Setting: HCG and GnRHa data were collected at My Duc Hospital, Ho Chi Minh City, Vietnam, and kisspeptin data were collected at Hammersmith Hospital, London, UK. Patients: Four hundred and forty nine women aged 18–38 years with antral follicle counts 4–87 were triggered with hCG (n = 161), GnRHa (n = 165), or kisspeptin (n = 173). Main outcome measure: Follicle sizes on the day of trigger most likely to yield a mature oocyte. Results: Follicles 12–19 mm on the day of trigger contributed the most to the number of oocytes and mature oocytes retrieved. Comparing the tertile of patients with the highest proportion of follicles on the day of trigger 12–19 mm, with the tertile of patients with the lowest proportion within this size range, revealed increases of 4.7 mature oocytes for hCG (P < 0.0001) and 4.9 mature oocytes for GnRHa triggering (P < 0.01). Using simulated follicle size profiles of patients with 20 follicles on the day of trigger, our model predicts that the number of oocytes retrieved would increase from a mean 9.8 (95% prediction limit 9.3–10.3) to 14.8 (95% prediction limit 13.3–16.3) oocytes due to the difference in follicle size profile alone. Conclusion: Follicles 12–19 mm on the morning of trigger administration were most likely to yield a mature oocyte following hCG, GnRHa, or kisspeptin.Publisher PDFPeer reviewe

Modelling Deformations in Car Crash animation

In this paper, we present a prototype of a deformation engine to efficiently model and render the damaged structure of vehicles in crash scenarios. We introduce a novel system architecture to accelerate the computation, which is traditionally an extremely expensive task. We alter a rigid body simulator to predict trajectories of cars during a collision and formulate a correction procedure to estimate the deformations of the collapsed car structures within the contact area. Non-linear deformations are solved based on the principle of energy conservation. Large plastic deformations resulting from collisions are modelled as a weighted combination of deformation examples of beams which can be produced using classical mechanics

Is a combination of varenicline and nicotine patch more effective in helping smokers quit than varenicline alone? A randomised controlled trial

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Examination of unremitting kidney illness by utilizing machine learning classifiers

Chronic kidney disease is a rising health issue that affects millions of people worldwide. Early detection and characterization of this disease is essential for effective management and control. This disease is associated with several serious health risks, such as cardiovascular disease, increased risk of stroke, and end-stage renal disease, which can be effectively prevented by early detection and treatment. Medical scientists rely on machine learning algorithms to diagnose the disease accurately at its outset. Recently, adding value to healthcare is being accomplished through the integration of machine learning algorithms into mobile health solution. Considering this, this paper proposes a predictive model of three machine learning classifiers, including Support Vector Machine, Decision Tree, and Multilayer Perceptron for chronic kidney disease prediction. The performance of the model was assessed using confusion matrix and executed in popular machine learning software tools such as WEKA and Rapid Minor. The study found that support vector machine yielded the highest accuracy rate of 98% in predicting chronic kidney disease in WEKA among other standard classifiers by using 10-fold cross validation. In addition, the proposed prediction model has been compared with existing models in terms of accuracy, sensitivity, and specificity. The experimental results indicate that the proposed predictive model shows promising results. These findings could integrate with the development of mobile health solution and other innovative approaches to prevent and treat this debilitating condition.info:eu-repo/semantics/acceptedVersio

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas

DNA damage repair (DDR) pathways modulate cancer risk, progression, and therapeutic response. We systematically analyzed somatic alterations to provide a comprehensive view of DDR deficiency across 33 cancer types. Mutations with accompanying loss of heterozygosity were observed in over 1/3 of DDR genes, including TP53 and BRCA1/2. Other prevalent alterations included epigenetic silencing of the direct repair genes EXO5, MGMT, and ALKBH3 in ∼20% of samples. Homologous recombination deficiency (HRD) was present at varying frequency in many cancer types, most notably ovarian cancer. However, in contrast to ovarian cancer, HRD was associated with worse outcomes in several other cancers. Protein structure-based analyses allowed us to predict functional consequences of rare, recurrent DDR mutations. A new machine-learning-based classifier developed from gene expression data allowed us to identify alterations that phenocopy deleterious TP53 mutations. These frequent DDR gene alterations in many human cancers have functional consequences that may determine cancer progression and guide therapy