Comparison of Perimetric Outcomes from Melbourne Rapid Fields Tablet Perimeter Software and Humphrey Field Analyzer in Glaucoma Patients

Purpose. To compare visual field results obtained using Melbourne Rapid Fields (MRF) iPad-based perimeter software and Humphrey Field Analyzer (HFA) 24-2 Swedish Interactive Threshold Algorithm (SITA) standard program in glaucoma patients. Design. A cross-sectional observational study. Methods. In this single-centre study involving patients diagnosed with glaucoma, the perimetric outcomes of MRF were compared against those returned from the HFA 24-2 SITA standard. Outcomes included mean deviation (MD), pattern standard deviation (PSD), visual field index (VFI)/visual capacity (VC), foveal threshold, test time, number of points depressed at P<5% on PSD probability plot, and glaucoma hemifield test/color coded indicator. Results. The study included 28 eyes of 28 glaucoma patients. Mean (standard deviation) test times were 342.07 (56.70) seconds for MRF and 375.11 (88.95) for HFA 24-2 SITA standard P=0.046. Mean MD was significantly lower for MRF (Δ = 3.09, P<0.001), and mean PSD was significantly higher for MRF (Δ = 1.40, P=0.005) compared with HFA. The mean foveal threshold for the MRF was significantly lower than the mean HFA foveal threshold ((Δ = 9.25, P<0.001). The number of points depressed at P<5% on the PSD probability plot was significantly less for MRF P<0.001. Other perimetric outcomes showed no significant differences between both. Bland–Altman plots showed that considerable variability existed between the programs. Conclusion. MRF is a good cost-effective, time-saving, user-friendly tool for monitoring visual fields in settings where access to traditional perimetry is limited. The lack of Internet strength in rural areas and questionable detection of early cases may be two points in MRF fields requiring an upgrade.

Regression equations of probability plot correlation coefficient test statistics using machine learning

&lt;p&gt;In the extremes hydrology field, it is essential to find the probability distribution model that is most appropriate for the sample data to estimate the reasonable probability quantile. Depending on the assumed probability distribution model, the probability quantile could be estimated with quite different values. The probability plot correlation coefficient (PPCC) test is one of the goodness-of-fit tests for finding suitable probability distributions for a given sample. The PPCC test determines whether assumed probability distributions are acceptable for the sample data using correlation coefficients between sample data and theoretical quantiles of assumed probability distributions. The critical values for identification are presented as a two-dimensional table, depending on the sample size and the shape parameters of models, for a three-parameter probability distribution. In this study, the applicability and utility of machine learning in the hydrology field were examined. For the usability of the PPCC test, a regression equation was derived using a machine learning algorithm with two variables: sample size and shape parameter.&lt;/p&gt;

Optimization of Different Workout Variables Throughout CNC Turning of ASTM 316 Deploying ANOVA

Blueprint of examination regarding ANOVA remains developed and executed for evaluating effect of various workout variables like V, F and D on surface unevenness throughout CNC turning of ASTM 316 steel using coated carbide insert. 3D graphs through momentous surface unevenness got developed and utilized for evaluating average surface unevenness through ideal design situations. Evidently, text interface impressions are extraneous. Research findings through different mathematical analyses provided the effective guideline for choosing appropriate machine settings to realize surface unevenness within the stipulated limit during stated turning operation. Ideal machining situations got determined to minimize the surface unevenness of same. Current research evidently divulges that multicoated carbide inserts performed marvelously at optimum workout variables combination of V = 150 m/min, F = 0.10 mm/rev with D = 0.4 mm. Ultimate range of Ra with Rz are 0.16 µm ≤ Ra ≤ 0.52 µm and 1.4 µm ≤ Rz ≤ 3.1 µm, respectively. Besides, Ra is below recommended safety limit 1.5 µm (i.e. Ra < 1.5 µm) for turning using coated carbide inserts. 3D surface plots got developed with changing 2 variables and fixing third one. Wholly, both unevenness variables (Ra and Rz) increase with F. Also, both unevenness variables (Ra and Rz) decrease with increase in V. But, D got quite insignificant impact on both unevenness variables (Ra and Rz). Probability plot of Ra is depicted for trialing statistical cogency of representations. Residuals discrepancies appear along approximately linear route

PENGARUH MODAL SENDIRI DAN MODAL PINJAMAN TERHADAP SISA HASIL USAHA (SHU) PADA KOPERASI UNIT DESA (KUD) BINA SEJAHTERA RENGAT BARAT KABUPATEN INDRAGIRI HULU

Tujuan penelitian ini adalah untuk mengetahui Modal Sendiri dan Modal Pinjaman secara simultan berpengaruh signifikan terhadap Sisa Hasil usaha (SHU), untuk mengetahui Modal Sendirisecara parsial berpengaruh signifikan terhadap Sisa Hasil usaha (SHU), untuk mengetahui Modal Pinjaman secara parsial berpengaruh signifikan terhadap Sisa Hasil usaha (SHU), di Koperasi Unit Desa Bina Sejahtera Rengat Kabupaten Indragiri Hulu. Dalam penelitian ini pola pikir yang digunakan pola pikir induktif. Analisis data menggunakan metode kuantitatif yaitu dengan asumsi klasik ,regresi linear berganda, koefisien korelasi dan determiniasi, uji F, dan uji t. Teknik pengumpulan data menggunakan interview dan studi pustaka. Berdasarkan hasil penelitian dan pembahasan maka dapat diambil kesimpulan yaitu : Dari perhitungan kuantitatif yang diperoleh dengan menggunakan rumus persamaan regresi linear berganda Y = a + b1X1 + b2X2 diperoleh Y = 21,458 + 0,32 X1 – 0,01 X2, yakni nilai koefisien konstanta (a) sebesar 21,458 hal ini berarti apabila nilai Modal Sendiri dan Modal Pinjaman sama dengan nol atau tetap, maka tingkat atau besarnya Sisa Hasil usaha (SHU) sebesar 21,458. Koefisien korelasi linear berganda (R) yaitu 0,872, artinya hubungan yang korelasi sangat kuat sekali antara Modal Sendiri dan Modal Pinjaman terhadap Sisa hasil Usaha (SHU) sebesar 0,872. Koefisien determinasi (R2) sebesar 0,761, artinya Sisa Hasil Usaha (SHU) dapat dijelaskan oleh variasi perubahan variabel independen Modal Sendiri dan Modal Pinjaman sebesar 76,1%, sedangkan sisanya 23,9% dijelaskan oleh variabel lain di luar penelitian. Berdasarkan hasil uji F diperoleh nilai Fhitung adalah 7,953 dengan tingkat signifikan 0,028. Sedangkan Ftabel pada taraf kepercayaan 95% (0,05) adalah 5,79. Pada kedua perhitungan Fhitung ˃Ftabel yaitu 7,953 ˃ 5,79. Hal ini berarti Ho ditolak dan Ha diterima, artinya secara simultan Modal Sendiri dan Modal Pinjamanberpengaruh signifikan terhadap Sisa Hasil Usaha (SHU). Berdasarkan hasil uji t diperoleh nilai thitung X1 ˃ ttabel = 2,635 < 2,44691 sehingga Ho ditolak dan Ha diterima, yang berarti variabel independen Modal Sendirisecara parsial berpengaruh signifikan terhadap Sisa Hasil Usaha (SHU). Selanjutnya nilai thitung X2 > ttabel = -1,288 > -2,44691 sehingga Ho ditolak dan Ha diterima, yang berarti variabel independen Modal Pinjaman secara parsial berpengaruh signifikan terhadap variabel Sisa Hasil Usaha (SHU). Berdasarkan hasil Uji Klasik diperoleh nilai Uji Multikolinieritas bahwa nilai tolerance dari kedua variabel independen lebih dari 0,1 yaitu VIF kurang dari 10 yaitu 1,328, maka dapat disimpulkan bahwa dalam model regresi tidak terjadi masalah multikolinieritas. Selanjutnya uji heteroskedastisitas diketahui bahwa titik-titik menyebar dengan pola yang tidak jelas diatas dan dibawah angka 0 pada sumbu Sisa Hasil Usaha (SHU) maka pada hasil ini tidak terjadi masalah heteroskedastisitas. Dan yang terakhir uji normalitas pada tampilan grafik histogram, didapatkan garis kurva normal, berarti data yang diteliti diatas berdistribusi normal. Demikian juga dari normal probability plot, menunjukan berdistribusi normal karena garis (titik-titik) mengikuti garis diagonal

Asymptotic Comparison of Parameters Estimates of Two-parameter Weibull Distribution

The Weibull distribution is frequently applied in various fields, ranging from economy, business, biology, to engineering. This paper aims at estimating the parameters of two-parameter Weibull distribution are determined. For this purpose, the method of quantiles (three different choices of quantiles) and Weibull probability plot method is utilized. The asymptotic covariance matrix of the parameter estimates is derived for both methods. For optimal random choices of quantiles, the variance, covariance and generalized variance is computed. The main contribution of this study is the introduction of the best choice of percentiles for the method of quantiles and the joint asymptotic efficiency comparison of applied methods.

Template Recycling and Reuse in MCM-41 Synthesis: Statistical Study

The recycle and reuse of template in MCM-41 synthesis were analysed using 23 full factorial design in order to study the effect of the template extraction parameters on the mass of MCM-41 powder produced. Four consecutive MCM-41 synthesis cycles utilizing the recycled template were studied with three factors that are ethanol fraction (A), amount of ion exchange agent (B) and the type of ion exchange agent (C). The significant effects contributed by the factors A, B and C and their interactions were identified through the half-normal probability plot and normal probability plot of the residuals. F-test and t-test were carried out to test the contribution of regression coefficients for synthesis cycles of MCM-41 synthesis models. AB interaction showed that larger mass of MCM-41 powder was obtained at high ethanol volume fraction and high quantity of ion exchange agent when either type of ion exchange agent was used.