Statistics And Probability Papers & Publications

As we aware diabetes is not only one kind of symptomatic disease but its occurrence spread through the various metabolic channels and hence raises other disorders. The prolonged symptoms of diabetes also cause the complications of eyesight, Night blindness, kidney failure, and other autoimmuno dysfunction including sexual dysfunction. In those symptomatic diseases various drugs are used to treat the diabetes such as biosimilar therapy including use of insulin and insulin analogues, oral hypoglycaemic agents and various other complementary medicines. As herbal remedies i.e. M charantia Linn. (Bitter Gourd) are commonly known as fruit vegetables. The Leaves, Seeds, Roots, Fruits and the stem part of the plants are medicinally used in different diseases. It is most effectively used to treat the acidic condition of gastrointestinal tract. M.charantia is also called the oxygen radical scavenger, which takes part into metabolic pathway. Due to the oxygen radical scavenging activity of GSH it directly expedites the ROS neutralization and the repair of ROS-induced damage which is important to neutralize the acidic condition of gastrointestinal tract.The present investigation was carried out to study the characterization of present antidiabetic compound having different solvent extract of M.charantia in various solvent system. The overall conclusion suggested that the extracted compound shows the antidiabetic and diuretic properties. The total unknown protein concentration was 21.01 µg/mL which is similar with standard antidiabetic drug and the slope consists of 0.0314 with the line of intercept 0.081, which has been elaborated in results and conclusion.

Dr. Wahul Umesh B

Ensuring consistent raw material quality is a significant challenge in chemical manufacturing, particularly for medicinal compounds where safety and efficacy are paramount. In these situations, a unique methodology known as Statistical Process Control (SPC) come into play. This study provides statistical process control analysis of four critical operational parameters for most the raw chemical compounds, especially in the medicinal chemistry— Specific Optical Rotation (SOR), Water Content (WC), RI, and Chromatographic Purity (CP)—derived from a dataset of 26 observations in an applied engineering context. The methodology encompasses descriptive statistics, rigorous distribution identification using Goodness-of-Fit tests, and process stability assessment via Individual- Moving Range (I-MR) and Exponentially Weighted Moving Average (EWMA) control charts. Descriptive statistics revealed diverse data characteristics, notably the high positive skewness (2.623) and kurtosis (9.386) of WC (Mean ± Standard Deviation: 0.177±0.106987) and the presence of negative values for SOR (Mean: -0.1, Min: -2, Max: 2). Distribution fitting identified Logistic and Normal as the most suitable for SOR, while RI demonstrated a best fit for normal distribution with Johnson Transformation. WC and CP exhibited significant non-normality and challenges in fitting standard distributions, often accompanied by warnings regarding convergence or parameter estimation stability. Crucially, control chart analysis identified significant out-of-control conditions for SOR, WC, and RI, indicating inherent process instability. CP, conversely, demonstrated stability with the optimized EWMA chart. The findings underscore the necessity of tailored statistical approaches for diverse data characteristics in quality control. Implementation of Statistical Process Control should not be underestimated in the chemical manufacturing industry, notably in the developing nations.

Mostafa Eissa

Implementation of Statistical Process Control (SPC) techniques in food and beverage industry are crucial to deliver consumable product that meets customer expectations. This study investigated industrial pH forecasting and process stability in a syrup manufacturing facility. We analyzed 1,247 pH observations with three objectives: (1) Quantify instability via control charts, (2) Model pH dynamics using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Classification And Regression Trees (CART), and (3) Develop diagnostic frameworks for unstable processes. Methodologically, Exponentially Weighted Moving Average (EWMA) charts assessed stability; Box-Cox transformed SARIMA (λ=2) with seasonal differencing was used for forecasting; CART identified variable importance. Control charts revealed profound instability: 83.3% of points violated 3σ limits; run tests significant (p<0.001). For SARIMA, (1,0,1)(0,1,1)₁₂ achieved significant parameters (p<0.0001) with improved residual diagnostics versus non-seasonal ARIMA, though minor autocorrelation remained at lag 12 (p=0.003). CART explained training R²=18.86% and test R²=17.93% of pH variation, identifying filling weight and sodium benzoate as key predictors. Crucially, this study demonstrates that forecasting retains diagnostic utility even in unstable environments: SARIMA residuals provide seasonal fingerprints of assignable causes, while CART thresholds guide intervention priorities. SARIMA(1,0,1)(0,1,1)₁₂ demonstrated superior residual properties: eliminated back forecast warnings (present in ARIMA), reduced autocorrelation at lag 24 (p=0.017 vs 0.040), and explicitly modeled 12-period seasonality. While process instability persists, SARIMA provides diagnostic fingerprints of assignable causes through seasonal parameters (SMA₁₂=0.9846, T=513.12) and residual patterns. We conclude that SARIMA offers enhanced short-term forecasting capability, but process intervention remains essential for reliability. The study advocates for integrated instability-informed forecasting combining SARIMA diagnostics, real-time control charts, and expanded sensor deployment.

Mostafa Eissa