Exchange rates are crucial in regulating the foreign exchange market's dynamics. Because of the unpredictability and volatility of currency rates, the exchange rate prediction has become one of the most challenging applications of financial time series forecasting. This study aims to build and compare the accuracy of various methods. The time series model Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) are utilized to forecast the daily US dollar to Pakistan rupee currency exchange rates (USD/PKR). Lagged observations of the data series and moving average technical analysis are used in both models. Explanatory factors were used as indicators, and the prediction performance was assessed using a variety of commonly known statistical metrics. These statistical metrics suggested the presence of conditional heteroscedasticity. Thus, the process turns to capture the volatility effect of conditional heteroscedasticity through GARCH modeling. It may be inferred based on the results of tentative models; that the ARCH model outperforms the GARCH model in terms of predicting the USD/PKR exchange rate.

Prostate cancer is a significant public health issue, ranking as the second most common cancer and the fifth leading cause of cancer-related deaths in men. In Pakistan, the prevalence of prostate cancer varies significantly across published articles. This study aimed to determine the pooled prevalence of prostate cancer and its associated risk factors in Pakistan. MEDLINE (via PubMed), Web of Science, Google Scholar, and local databases were searched from inception until March 2023, using key search terms related to the prevalence of prostate cancer. We considered a random-effects meta-analysis to derive the pooled prevalence and relative risks with 95% CIs. Two investigators independently screened articles and performed data extraction and risk of bias analysis. We also conducted meta-regression analysis and stratification to investigate heterogeneity. This study protocol was registered at PROSPERO, number CRD42022376061. Our meta-analysis incorporated 11 articles with a total sample size of 184,384. The overall pooled prevalence of prostate cancer was 5.20% (95% CI: 3.72–6.90%), with substantial heterogeneity among estimates (I² = 98.5%). The 95% prediction interval of prostate cancer was ranged from 1.74%–10.35%. Subgroup meta-analysis revealed that the highest pooled prevalence of prostate cancer was in Khyber Pakhtunkhwa (8.29%; 95% CI: 6.13–10.74%, n = 1), followed by Punjab (8.09%; 95% CI:7.36–8.86%, n = 3), while the lowest was found in Sindh (3.30%; 95% CI: 2.37–4.38%, n = 5). From 2000 to 2010 to 2011–2023, the prevalence of prostate cancer increased significantly from 3.88% (95% CI: 2.72–5.23%) to 5.80% (95% CI: 3.76–8.24%). Our meta-analysis provides essential insights into the prevalence of prostate cancer in Pakistan, highlighting the need for continued research and interventions to address this pressing health issue.

Background/Objectives: Diabetic retinopathy (DR) is a major microvascular complication of type 1 diabetes mellitus (T1DM) and remains an important cause of preventable visual impairment. Region-specific data on the incidence and clinical predictors of DR among patients with T1DM in Saudi Arabia remain limited. This study aimed to determine the incidence of DR and identify associated demographic and systemic risk factors among patients with T1DM at a tertiary care center in Riyadh, Saudi Arabia. Methods: This retrospective cohort study included 449 patients with T1DM aged ≥9 years who were followed at King Abdulaziz Medical City, Riyadh, between 2015 and 2025. Patients were selected using a consecutive non-probability sampling technique. Data were extracted from the BESTCare 2.0A electronic medical record system and supplemented, when required, by phone-based interviews to verify selected clinical and demographic variables. Patients were classified as controls without DR or cases with DR, including non-vision-threatening DR and vision-threatening DR (VTDR), according to the International Clinical Diabetic Retinopathy Severity Scale. Multivariable logistic regression, Cox proportional hazards models, and temporal trend analysis were performed, with statistical significance set at p < 0.05. Results: The overall incidence rate of DR was 92.66 per 1000 person-years, with similar rates among males and females. In multivariable logistic regression, older age at T1DM diagnosis, longer diabetes duration, hypertension, hyperlipidemia, and albuminuria were independently associated with DR. Mean HbA1c and HbA1c variability were not independently associated with DR after adjustment. In Cox regression, older age at T1DM diagnosis was associated with higher hazards of both DR and VTDR, while hypertension was associated with VTDR. Among patients with DR, younger age at T1DM diagnosis was associated with higher odds of proliferative disease in exploratory severity analysis. Conclusions: DR was common among patients with T1DM in this tertiary-care cohort and was mainly associated with disease duration, age at diagnosis, and systemic vascular comorbidities. These findings support the importance of routine ophthalmologic screening and integrated management of systemic risk factors in patients with T1DM.

In test cricket, we rated batters performance. We have proposed rating criteria as introduced by Scarf, Akhtar, and Rasool in 2014 with additional explanatory variables on the updated data set. The newly added covariates that we included in our research are the home factor and the ground influence. The same rating system is applied in the previous study. Using multinomial logistic regression, sessions from all days of a test match are modeled to determine match outcome probabilities at the end of each session. These models are based on all of the factors that can influence the outcome of a match. It is discovered that the predictors of home factor and pitch quality have a significant impact on the outcome of the test match. We used multinomial logistic regression to model data and estimate the parameters in the models. We forecasted match outcomes using these models at the end of each session and measured batters performances by using these probabilities. This process is repeated in a test match at the end of a session, and batters contributions to their team score are accumulated. Both teams batters are then ranked based on their rating points. The batsmen are rated based on their performance in the match by adding new factors (pitch effect and home advantage) in the models. The proposed ranking is compared with the ICCs traditional ranking of batters in the test cricket series.