Anderson-Darling Research Articles

Application of Statistical Methods for the Characterization of Radon Distribution in Indoor Environments: A Case Study in Lima, Peru

This study evaluates the effectiveness of advanced statistical and geospatial methods for analyzing radon concentration distributions in indoor environments, using the district of San Martín de Porres, Lima, Peru, as a case study. Radon levels were monitored using LR-115 nuclear track detectors over three distinct measurement periods between 2015 and 2016, with 86 households participating. Detectors were randomly placed in various rooms within each household. Normality tests (Shapiro–Wilk, Anderson–Darling, and Kolmogorov–Smirnov) were applied to assess the fit of radon concentrations to a log-normal distribution. Additionally, analysis of variance (ANOVA) was used to evaluate the influence of environmental and structural factors on radon variability. Non-normally distributed data were normalized using a Box–Cox transformation to improve statistical assumptions, enabling subsequent geostatistical analyses. Geospatial interpolation methods, specifically Inverse Distance Weighting (IDW) and Kriging, were employed to map radon concentrations. The results revealed significant temporal variability in radon concentrations, with geometric means of 146.4 Bq·m−3, 162.3 Bq·m−3, and 150.8 Bq·m−3, respectively, across the three periods. Up to 9.5% of the monitored households recorded radon levels exceeding the safety threshold of 200 Bq·m−3. Among the interpolation methods, Kriging provided a more accurate spatial representation of radon concentration variability compared to IDW, allowing for the precise identification of high-risk areas. This study provides a framework for using advanced statistical and geospatial techniques in environmental risk assessment.

Cost-Effective Laser Powder Bed Fusion of Ti-6Al-4V Grade 5: The Effect of Expanding Powder Size Distribution on Mechanical Performance

This study aims to assess the feasibility of expanding the powder size distribution (PSD) of Ti-6Al-4V grade 5 powder for LPBF to achieve cost reduction. Parameter optimization to minimize the degradation of mechanical properties due to the expanded particle size distribution was conducted. Mechanical tests for specimens built using optimized parameters revealed minor reductions in strength: 3.9% in tensile yield strength, 1.1% in compressive strength, 5.5% in shear strength, and 4.5% in bearing yield strength—all of which complied with MMPDS standards. Statistical analysis, using the Anderson–Darling test, demonstrated stable mechanical performance and minimal variation between the original and expanded PSDs. These results highlight the potential of an expanded PSD to achieve cost reductions while maintaining compliance with industry standards, offering a practical solution for LPBF applications in cost-sensitive and high-performance industries.

Novel multiple Z-score models for detection of coronary artery dilation: application in Kawasaki disease

BackgroundThis study aims to develop Z-Score models to normalize measurements of three coronary arteries and enhance the diagnosis of Kawasaki disease (KD) in children from newborns to 10 years old. Developing a reliable Z-Score model is challenging, as some existing models fail the normality test. Overcoming these challenges is crucial for improving KD diagnosis.MethodDetailed measurements of the left main coronary artery (LCA), left anterior descending coronary artery (LAD), and right coronary artery (RCA) were collected, along with patient demographics such as age, height, weight, and body surface area (BSA). Several Z-Score models, named the Kuo Z-Score models, were proposed, with separate designs for different coronary arteries and different age groups, resulting in multiple Z-Score models. The Z-Score model for the RCA employs the Box-Cox method for data transformation. Finally, we tested various age group combinations, selecting models that passed the Anderson–Darling normality test and had higher R-square values for robustness and best data fit.ResultsThe study included 1180 participants free from coronary or heart diseases. The Kuo Z-Score models were optimized for LCA, LAD, and RCA across the five age groups 0–6 years, 6–7 years, 7–8 years, 8–9 years, and 9–10 years. Only the normality test for the RCA in the 7–8 year age group failed. The proposed model fitted to the normality assumption outperforming the other models.ConclusionThe Kuo Z-Score models, applicable across a broad age range, provides robust identification of coronary artery dilatation and aneurysm in KD. The models’ capability to normalize diverse data sets marks a significant advancement in KD diagnostic sensitivity, aiding in better clinical decision-making and potentially improving patient outcomes.

Determining the Correlation of Fat-Free Mass, Power, and Maximum Hamstring Force in Female Basketball Athletes

Background. In competitive sports, especially basketball, physical attributes such as agility, speed, strength, and power are crucial for performance. Objectives. This study aimed to examine the correlation between fat-free mass (FFM), countermovement jump (CMJ) power, and maximum hamstring force in female basketball athletes, providing insights that could enhance performance and reduce injury risk. Materials and methods. This observational cross-sectional study investigates the relationship between fat-free mass (FFM), power, and maximum hamstring force in 45 female basketball athletes. The Anderson-Darling test for assessing normality and Spearman’s correlation for non-normally distributed data were used to conduct the study. Results. Significant correlations were found between FFM and maximum hamstring force in the both right (r = 0.585, p < 0.001) and left legs (r = 0.522, p < 0.001), as well as between (CMJ) power and maximum hamstring force for both legs (right: r = 0.467, p = 0.001; left: r = 0.434, p = 0.003). Additionally, power was significantly correlated with jump height (r = 0.604, p < 0.001). Conclusions. These findings suggest that FFM and power are critical determinants of hamstring strength and explosive performance in female basketball athletes.

The dynamical lineage of isolated, HI-rich ultra-diffuse galaxies

Ultra-diffuse galaxies (UDGs) exhibit morphological similarities with other low-luminosity galaxies, indicating a possible evolutionary connection. We investigated for the common dynamical characteristics of isolated, HI-rich UDGs with other low-luminosity field galaxies, namely the low surface brightness galaxies (LSBs) and the dwarf irregulars (dIrrs). We considered samples of each of the UDGs, LSBs, and the dIrrs. We first obtained scaling relations involving mass and structural parameters for the LSB and the dIrr samples and superposed the UDGs on them. We then carried out a two-sample Anderson-Darling test to analyse whether the UDGs belong to the population of the LSBs or the dIrrs. Thereafter, we constructed distribution function-based stellar-dynamical models of these galaxies to determine their kinematical parameters. We followed up with the Mann-Whitney U-test to determine if our UDG, LSB, and dIrr samples belong to different parent populations so far as kinematics is concerned. Finally, we conducted principal component analyses involving both structural and kinematical parameters to identify the key properties accounting for the variance in the data for the respective galaxy populations. From the galaxy scaling relation studies, we note that the UDGs and the LSBs constitute statistically different populations. However, for the UDGs and the dIrrs, the null hypotheses of these statistical tests cannot be rejected for the following scaling relations: (i) stellar mass versus atomic hydrogen mass, (ii) stellar mass versus dynamical mass, and (iii) dark matter core density versus core radius mass scaling. Interestingly, the dynamical models suggest that the UDGs, LSBs, and the dIrrs constitute different galaxy populations, as reflected by their radial-to-vertical velocity dispersion and the rotational velocity-to-total stellar velocity dispersion. Finally, we observe that the total HI and stellar mass mostly regulate the variance in the structural and kinematical data for both the UDGs and the dIrrs, while the ratio of radial-to-vertical velocity dispersion and the total HI mass dominate the variation in the LSBs. The UDGs and the LSBs represent statistically different galaxy populations with respect to their mass and structural properties. However, the statistical studies do not negate the fact that the structural parameters of the UDGs and the dIrrs follow the same normal distributions. However, the UDGs, LSBs, and the dIrrs constitute very different populations as far as their kinematical parameters are concerned. Finally, we note that the variation in the structural and kinematical data of both the UDGs and the dIrrs is mostly accounted for by their stellar mass and HI mass, whereas for the LSBs, the variance is explained by the ratio of the radial-to-vertical stellar dispersion followed by the HI mass. Thus, we may conclude that the UDGs and dIrrs share a common dynamical lineage.

Insights into the Long-term Flaring Events of Blazar PKS 0805-07: A Multiwavelength Analysis Over the Period of 2009–2023

We conducted a comprehensive temporal and spectral study of the FSRQ PKS 0805-07 by using broadband observations from the Fermi-LAT and Swift-XRT/UVOT instruments over the period of MJD 54684-60264. The 3 day binned γ-ray light curve during the active state revealed 11 distinct peak structures with the maximum integral flux (E > 100 MeV) reached (1.56 ± 0.16) × 10−6 photons cm−2 s−1 on MJD 59904.5. The shortest observed γ-ray variability was 2.80 ± 0.77 days. A correlation analysis between the γ-ray spectral index and flux indicated the typical trend of hardening when the source is brighter, commonly observed in blazars. We identified a lag of 121 (+27.21, −3.51) days in the spectral index relative to the flux, within the time interval MJD 59582 to 60112. The Anderson–Darling test and histogram fit rejected the normality of the γ-ray flux distribution and instead suggested a log-normal distribution. To gain insight into the underlying physical processes, we extracted broadband spectra from different time periods in the light curve. The spectral energy distribution during various flux states was well-reproduced using synchrotron, synchrotron-self-Compton, and external-Compton emissions from a broken power-law electron distribution. The seed photons required for the external Compton process are from the IR region. A comparison of the best-fit physical parameters indicated that the variations in different flux states were primarily associated with an increase in the bulk Lorentz factor and magnetic field from low to high flux states.

Fitting Probability Distributions and Statistical Trend Analysis of Rainfall of Agro-climatic Zone of West Bengal

This research aimed to identify the most appropriate probability distribution for modeling average monthly rainfall in the agro-climatic zones of West Bengal and to detect any trends in this data. The study utilized historical rainfall data spanning 51 years (1970-2020) obtained from the IMD in Pune. To determine the best-fitting distribution and assess trends, 23 different probability distributions were employed, with the Mann-Kendall test and Sen’s slope estimator used for trend analysis. Goodness-of-fit tests, including the Kolmogorov-Smirnov, Anderson-Darling, and Chi-square tests, were employed to determine the most suitable distribution. The findings indicated that the Generalized Extreme Value, Gamma, and Lognormal (3-parameter) distributions were the best fits for two specific districts. The monthly rainfall distributions can be effectively used for predicting future monthly rainfall events in the region. The Mann-Kendall test revealed an increasing trend in rainfall for Kalimpong and Nadia Districts and a decreasing trend for Malda District.

Assessing the Suitability of Probability Distribution Models for Streamflow Analysis in Peninsular Malaysia

Streamflow analysis is indeed a need for Malaysia which always threaten by extreme streamflow events including flooding. However, the determination of the most suitable probability distribution for streamflow analysis has been the major concern and received considerable critical attention recently. The purpose of this study is to analyse the performances of several distributions in fitting the streamflow data collected form 11 streamflow stations in Peninsular Malaysia. and select the best performed distribution eventually. Normal, Generalized Extreme Value (GEV), three-parameter Gamma (G3), two-parameter Gamma (G2), three-parameter Weibull (W3), two-parameter Weibull (W2), three-parameter Log-Normal (LN3) and two-parameter Log-Normal (LN2) were used in this study to fit the maximum, minimum, and mean streamflow with monthly and seasonal time scales by using Easyfit software. The performances of distributions were evaluated through constructing Probability-Probability (P-P) Plot, Cumulative Distribution Function (CDF) and conducting goodness-of-fit tests including Chi-square () Test, Anderson-Darling (AD) Test and Kolmogorov-Smirnov (KS) Test. All the results were tabulated for comparison and the most suitable probability distribution was selected. Overall, the results indicated that the GEV distribution was the best fit distribution for most of the stations and most of the data series or time scales, while the LN3 distribution appeared to be the second-best distribution. These findings add to a growing body of literature on the selection of probability distribution especially for the streamflow analysis in Peninsular Malaysia. Besides, these findings provide significant information and practical knowledge in supporting the future development of streamflow-related plans or management including the flood and drought mitigation plans.

A next generation probabilistic approach to analyze cancer patients data with inference and applications

This study addresses the critical challenges faced in cancer care, particularly in predicting survival times for patients with lung cancer and acute myeloid leukemia. Despite recent advancements in medical science, existing models often fall short in accurately capturing disease progression, leading to less effective clinical decision-making, and compromised patient outcomes. The need for advanced predictive models is urgent to improve survival time forecasts and enhance treatment strategies. In response to this, we introduce a novel probabilistic approach, the New Weibull (NEWE) model, which is part of a newly generated class designed to model cancer patient data more effectively. Our methodology includes using seven well-known estimation methods, each rigorously evaluated for consistency through Monte Carlo simulation studies focused on key metrics such as absolute bias, mean square error, and mean relative error. The datasets analyzed include survival times for twenty acute myeloid leukemia patients, 121 breast cancer patients from 1929 to 1938, 33 patients with acute myelogenous leukemia, data from eighteen individuals who died from causes unrelated to cancer, and survival times of advanced lung cancer patients undergoing standard chemotherapy. The NEWE model outperformed competing models, particularly in Anderson-Darling, Cramer-von Mises, and Kolmogorov-Smirnov tests, with significantly higher p-values. These findings highlight the NEWE model’s potential to transform predictive oncology by offering more precise survival time predictions, improving the quality of care and decision-making in cancer treatment.

A Multi-Model Survival Analysis of Lung Cancer Using Parametric Techniques

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, underscoring the critical need for effective prognostic tools. This study utilizes survival analysis to explore the factors that influence the survival outcomes of North Central Cancer Treatment data related to lung cancer. The main goal of this study is to compare and contrast various statistical models, including the Weibull, Exponential, Log-gaussian, Gumbel, and Rayleigh models. We have computed important functions, such as the survival function, the hazard function, and the cumulative hazard function, for all the considered distributions. The Anderson-Darling and Cramer Von-Mises tests, which are Goodness of fit tests, effectively compare and assess various parametric regression survival models. The Weibull survival model is interpreted to be the most effective and efficient way to study the lung cancer dataset, which is concluded upon evaluating the results of Anderson-Darling statistic 0.28745, Cramer Von-Mises statistic 0.0450, Mean Survival Probability 0.9697, Mean Cumulative Survival Probability 0.0303, Akaike Information Criterion 1,650.753 and Bayesian Information Criterion 1623.329 of the Weibull, Exponential, Log-gaussian, Gumbel, and Rayleigh parametric regression survival models.

Modified two-sample Anderson-Darling test statistic

Two-sample testing hypothesis has been used and discussed in several scientific fields. However, the underlying theoretical distribution cannot be assumed to have a specific parametric distribution such as a normal distribution. When it is difficult to assume an underlying distribution, non parametric statistical methods are preferable. Such methods are typically robust and require fewer assumptions regarding the underlying distribution. As it is difficult to determine the number of parameters in an unknown distribution, we are interested in testing whether two independent samples of distributions are equal. Although the power of the Kolmogorov-Smirnov test statistic is not high, it is the standard test statistic for this general alternative. However, there are some advantages in using the Anderson-Darling test statistic instead of the Kolmogorov-Smirnov test statistic. We introduce two modified Anderson-Darling test statistics and list the exact critical values for small sample sizes. Moreover, we derive the limiting distribution of the proposed test statistic. Furthermore, we investigate the power performances of the proposed test statistic in various scenarios using Monte-Carlo simulations. Simulation studies and real data analysis reveal that the proposed test statistic is a good competitor to existing omnibus test statistics and has some advantages in certain situations.

Generating Rainfall IDF Curves Using IMD Reduction Formula and Choosing the Best Distribution for Babylon City, Iraq

ABSTRACTIntensity‐duration‐frequency (IDF) models are considered one of the most important tools used in water resources projects, as well as the design and planning of hydraulic structures such as sewerage channels, bridges, culverts, and road networks. This study aims to generate IDF curves for the Iraqi city of Babylon based on the Indian Meteorological Department (IMD) empirical reduction formula and to choose the optimal distribution that gives the greatest rainfall intensity among the three distributions used in this research (generalized extreme value, Log‐Pearson type III, and Gumbel). This study examined daily rainfall data collected from the Iraqi Authority of Meteorology and Seismic Monitoring for a period of 32 years, from 1991 to 2022. The IMD reduction formula was used to calculate rainfall intensity for shorter durations (5, 10, 20, 30, 60, 120, 360, 720, and 1440 min) and custom return periods (2, 5, 10, 25, 50, and 100 years). To determine the goodness of fit for the data distributions, the Easy Fit 5.6 program was applied with three tests (the χ2 test, the Anderson–Darling test, and the Kolmogorov–Smirnov test). The results showed that all distributions were acceptable for both tests and all storm durations and that the rainfall intensity decreased with increasing duration of the rainstorm. It also showed that rainfall increases rainfall intensity during large return periods. Based on the criteria of AIC and BIC, the LP‐3 distribution was chosen as the best distribution to simulate rainfall in Babylon City using the IDM reduction formula.

Fitting the Seven-Parameter Generalized Tempered Stable Distribution to Financial Data

This paper proposes and implements a methodology to fit a seven-parameter Generalized Tempered Stable (GTS) distribution to financial data. The nonexistence of the mathematical expression of the GTS probability density function makes maximum-likelihood estimation (MLE) inadequate for providing parameter estimations. Based on the function characteristic and the fractional Fourier transform (FRFT), we provide a comprehensive approach to circumvent the problem and yield a good parameter estimation of the GTS probability. The methodology was applied to fit two heavy-tailed data (Bitcoin and Ethereum returns) and two peaked data (S&P 500 and SPY ETF returns). For each historical data, the estimation results show that six-parameter estimations are statistically significant except for the local parameter, μ. The goodness of fit was assessed through Kolmogorov–Smirnov, Anderson–Darling, and Pearson’s chi-squared statistics. While the two-parameter geometric Brownian motion (GBM) hypothesis is always rejected, the GTS distribution fits significantly with a very high p-value and outperforms the Kobol, Carr–Geman–Madan–Yor, and bilateral Gamma distributions.

АНАЛІЗ МАЛИХ ВИБІРОК БАГАТОВИМІРНИХ ДАНИХНА ПРИКЛАДІ ТЕРМІНІВ ОДУЖАННЯ ПАЦІЄНТІВ ВІД COVID-19

The analysis of multidimensional data presented in small volumes - small samples is quite often used in publishing, pedagogy, sociology mainly for the purpose of understanding the existing situation and choosing and making appropriate decisions. The main goal of such an analysis is to identify the impact of various factors on the object of attention in order to adjust further steps in one or another type of activity. The authors of this article worked with the material, namely with the medical and biological indicators of 19 patients with the disease of Covid-19, who underwent a course of treatment and at the time of recovery, their condition is presented with relevant data. These data include: duration of recovery of each patient in bed days and 29 indicators of physical and physiological state, divided into five groups. These are the following groups: physical characteristics (age, height, weight) and cardiovascular system, respiratory system, immune and circulatory systems. The data analysis started with the recovery term regarding the normality of the distribution. The following methods were used for this: quartile-quartile graph, as well as Shapiro-Wilk, Kolmogorov-Smirnov and Anderson-Darling tests. Based on the results of the application of these methods, it was concluded that the data are subject to the normal distribution law. The presence of both women and men in the data sample, and the latter are twice as few, required clarification of the gender difference between them. Here, the authors used a visual comparison using a boxplot and Student's T-test. By visual comparison, the differences between the minimums and the first quartiles are greater than the differences between the medians, third quartiles, and maximums, but according to the results of the T-test, the average values ​​of these samples are equal and differ only by chance. The relationship of signs with the terms of recovery was determined in the following way. For each group of features, their individual multivariate averages were determined, and the recovery time interval was divided into four subintervals. According to these subintervals, patients were divided along with their multivariate averages for each group of indicators. Each subinterval is matched with the average value of the individual multivariate averages. The data are presented in a table - subintervals as levels of the main factor and groups of averages from individual multivariate averages as variables. One-factor variance analysis was applied to the data in the table, the equality of the average groups of indicators, as well as the weakness of the influence of diagnostic indicators on recovery time. The work has a scientific and practical nature and can be useful in similar situations.

Assessing wind energy exploitation potential in several regions of Viet Nam using Kernel density estimation model

This article analyzes and assesses the potential for wind energy exploitation in six regions of Vietnam. The wind speed data are used to construct wind speed probability distributions (WSPDs) based on kernel density estimation (KDE). The KDE distribution, with six bandwidth selection methods, is implemented to generate probability density functions (PDFs) for each region's data to describe wind speed characteristics. The statistical tests Cramér-Von Mises (CvM), Anderson-Darling (A-D), and Kolmogorov-Smirnov (K-S) are applied to evaluate the PDFs' goodness-of-fit performance. The analysis results present the KDE distribution using the least-squares cross-validation (LSCV), and the Scott bandwidth selection method has outstanding fitting performance. Based on these PDF distributions, the wind turbine (WT) power curve is used to estimate and predict the amount of electricity that can be produced. This study also proposes a reliable method for wind power output planning based on wind speed that can be universally applied.

Bayesian mixture model for accurate assessment of monthly maximum wind speed: A case study in Gwadar

Assessment of monthly maximum wind speed (MMWS) is essential in various environmental disciplines, including architectural risk assessment, climatology, renewable energy sources, building design, and agricultural activities. The distribution of wind speed is an important module of its development. Presently, several mixing distributions have been suggested for fitting the theoretical maximum wind speed distributions. However, the drawback of various distribution elements is the outcome of numerous fitting presentations, and it is challenging to choose the best form of single distribution to employ when building a hybrid model. In order to solve those difficulties, utilizing the mixtures of Sine-skewed-von Mises distributions (MSS-VM) with various prior parameter distributions is extended in the present study. In this research, the assessment of MMWS employing the maximum-likelihood estimation (MLE) method is taken into consideration for the wind speed data from the Pakistan province of Baluchistan Gwadar station. The results of the Anderson Darling (AD), Chi-square, BIC, and AIC tests, as well as the Kolmogorov Smirnov (KS) test, indicated that MSS-VM was the best distribution for the Gwadar station. The suggested models automatically find the optimum number of elements, in addition to having more appropriate performance. The outcomes demonstrate that MSS-VM performance is more suitable than other heterogeneous and single-distribution models. The design estimations derived using the Bayesian mixture distribution provide a sense of the highest wind speed that will occur across a particular area. It is therefore crucial for designers and policymakers in the planning, designing, and building of various structures.

Statistical Analysis and Several Estimation Methods of New Alpha Power-Transformed Pareto Model with Applications in Insurance

This article defines a new distribution using a novel alpha power-transformed method extension. The model obtained has three parameters and is quite effective in modeling skewed, complex, symmetric, and asymmetric datasets. The new approach has one additional parameter for the model. Certain distributional and mathematical properties are investigated, notably reliability, quartile, moments, skewness, kurtosis, and order statistics, and several approaches of estimation, notably the maximum likelihood, least square, weighted least square, maximum product spacing, Cramer-Von Mises, and Anderson Darling estimators of the model parameters were obtained. A Monte Carlo simulation study was conducted to evaluate the performance of the proposed techniques of estimation of the model parameters. The actuarial measures are computed for our recommended model. At the end of the paper, two insurance applications are illustrated to check the potential and utility of the suggested distribution. Evaluation using four selection criteria indicates that our recommended model is the most appropriate probability model for modeling insurance datasets.

Nitrogen metabolism in blackmoiled cattle of Kuzbass

A research analysis of nitrogen metabolism in black-and-white cattle was carried out in the conditions of the Kuzbass region. Healthy animals at the age of 2nd lactation with a productivity level from 7300 to 10500 kg were selected for the experimental group. Laboratory studies were carried out at the Department of Veterinary Genetics and Biotechnology of the Novosibirsk State Agrarian University. Indicators of nitrogen metabolism in blood serum were assessed photometrically on a biochemical semi-automatic analyzer Photometer 5010 V5+. Reagent kits from Vector-Best and Olvex-Diagnosticum were used. When biometric data processing, standard descriptive statistics techniques and robust indicators were used. The Anderson-Darling test was used to assess the normality of data distribution. .When calculating correlation relationships, the Pearson coefficient was used. Calculations were performed in the “RStudio” programming environment. The obtained average values of nitrogen metabolism indicators were within the physiological norm. The strongest positive associations were observed between urea levels and uric acid, and urea and creatinine. No connection was found between other indicators of nitrogen metabolism. There were no differences in milk yield between cows with high and low levels of nitrogen metabolism. Graphs of the range of nitrogen content in black-and-white cows during lactation are presented. Calculations of reference indicators based on data from Kuzbass dairy cattle make it possible to more accurately assess the clinical condition of animals and timely detect deviations from established standard values. It is also necessary to take into account reference limits when monitoring the quality and completeness of cattle.

The predictive role of Systemic Inflammation Response Index (SIRI), Neutrophil-Lymphocyte Ratio (NLR), and Platelet-Lymphocyte Ratio (PLR) in the prognosis of acute coronary syndrome in a tertiary care hospital

Background& Objective: Acute coronary syndrome (ACS) is a major cause of mortality globally, with significant morbidity and economic impact. This study aimed to correlate the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic inflammatory response index (SIRI) values in ACS patients with their prognosis via the GRACE scoring criteria and to propose the SIRI as a superior inflammatory marker for predicting ACS prognosis. MethodsEthical approval was obtained for a retrospective cross-sectional study, which included patients from the outpatient department and Tamilnadu Accident Emergency Initiative Ward at Government Medical College, Omandurar Government Estate, who were diagnosed with ACS according to American College of Cardiology guidelines from January 2022 to December 2023. We excluded patients with familial hypercholesterolemia, platelet disorders, infections, inflammatory conditions, or incomplete health records. Data on demographics, clinical findings, blood counts, ECGs, cardiac enzymes, echocardiography, serum creatinine, and vital signs were collected and analyzed to calculate the NLR, PLR, SIRI, and GRACE scores. Statistical analyses included Kolmogorov‒Smirnov and Anderson‒Darling tests, Spearman correlation, Kruskal‒Wallis one-way ANOVA, GLM modeling, k-fold cross-validation, and receiver operating characteristic (ROC) curve analysis. ResultsAfter applying the exclusion criteria, 247 ACS patients were included in the analysis. Significant associations were found between the NLR and the PLR, SIRI, and GRACE scores. The SIRI demonstrated the strongest association, whereas the PLR had the weakest association. All three variables significantly influenced prognostic risk, as determined by the GRACE score. GLM models highlighted the predictive significance of the NLR, PLR, and SIRI in estimating GRACE scores, with the SIRI showing potential superiority. K-fold cross-validation confirmed the superior predictive accuracy and ability of the SIRI to explain a larger proportion of variance in GRACE scores than the NLR and PLR. ConclusionsThe SIRI emerges as a promising prognostic marker for ACS, outperforming the NLR and PLR. Its ease of calculation from routine hemogram tests underscores its potential clinical utility for risk stratification in ACS management. Further validation and integration into existing risk assessment models could enhance prognosis assessment in ACS patients.

Evaluation of probability distribution methods for flood frequency analysis in the Jhelum Basin of North-Western Himalayas, India

The Kashmir Valley has frequently endured devastating floods, presenting significant challenges for flood management due to unpredictable flood magnitudes and limited pre-disaster preparedness. A major difficulty in addressing these challenges is the lack of reliable flood frequency analysis (FFA) for effective planning and mitigation. This study seeks to overcome these issues by employing a rigorous quantitative analysis of annual peak discharge data over a 51-year period (1971–2021). One key challenge was the presence of low outliers, which could compromise the integrity of the data. To address this, the Multiple Grubbs-Beck test was applied to remove these outliers before conducting FFA. The study's original achievement lies in its application of multiple distribution models which include Gumbel (EV1), Generalized Extreme Variations (GEV), Log-Normal, Log Pearson III (LP III), Gamma and Normal distribution. Goodness-of-fit tests, including Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and Chi-square at the 5 % significance level, along with visualization techniques such as Probability plots (PP), Quantile plots (QQ), and Probabilistic distribution (PD) graphs, were used to identify the most suitable distribution methods. The Log Pearson Type III (LP-III) was identified as the best fit for the Sangam gauge site (Upper Jhelum), the gamma distribution for Ram Munshibagh (Middle Jhelum), and the Generalized Extreme Value (GEV) and LP-III for Asham (Lower Jhelum). For Sangam, the estimated discharges for 2, 5, 10, 50, 100, 150, 200, and 250-year return periods were 549.63, 1028.43, 1471.34, 2907.64, 3758.92, 4338.61, 4790.99, and 5167.23 cumecs, respectively, using LP-III. For Ram Munshibagh, the discharges were 602.13, 911.03, 1107.04, 1512.12, 1674.35, 1767.0, 1831.87, and 1881.74 cumecs using the gamma distribution. For Asham, the discharges were 685.8, 998.0, 1193.3, 1593.2, 1750.6, 1839.4, 1901.0, and 1948.0 cumecs using the GEV distribution. The findings indicate that the Jhelum River cannot accommodate excess discharge for return periods of 5 years or more, underscoring the need for enhanced flood management strategies.