This study intensively examined the monthly water consumption forecasting performance using advanced time series (ARIMA) models. Thus, this study intends to identify the appropriate ARIMA models to best fit the water consumption data in Southwestern Ethiopia Tepi town and forecast water consumption effectively in the city. The data used for this study was the monthly water consumption in Tepi town from January 2016 to December 2021.The data were converted to returns to enhance their statistical properties and the returns were used to fit a mean equation. The monthly average water consumption in Tepi Town is 77227.8 meters cubic. Both original and transformed data show the trend of water consumption is increasing over time. Several ARIMA models were fitted to the data, and it emerged that the most adequate model for the data was ARIMA (1, 1, 1) based on the model selection criterion. The parameters for ARIMA models were estimated using the Ordinary Least Squares Estimation (OLS) method. The model was used to forecast the consumption for the next ten months and to advise Tepi town Water Company Limited in the city to meet the demand of the people. Conclusion. The consumption of water is increasing from December to September.
In any organization, the capability, knowledge and skills play a significant role in success of an employee. For instant, in hospital management, the performance evaluation of an employee is mainly based on qualitative in nature. Evaluation of certain factors such as uncertainty, vagueness and imprecision is based on the decision making ability of the evaluator. The performance evaluation for promotion, incentives, bonuses, growth and development of any employee should be recognized in effective and efficiency manner with appropriate ratings. Therefore, this paper aims to design and implement a multi criteria performance evaluation for hospital employees to get promotion, incentives, bonus, growth and development. In this paper, we propose an Interval Valued Fuzzy Weighted Distance Algorithm (IVFWDA) for performance evaluation. The expected interval and the actual interval of the work achievement, management skill, personal quality, care, safety and risk management features are extracted by Interval Valued Fuzzy Soft Matrix (IVFSM) and it is effectively done using the proposed algorithm. An example is presented as a case study to illustrate the proposed algorithm. Implementation of the proposed algorithm in Maple is also discussed with sample computations.
In estimating the parameters of the five unknown parameters Single‐Diode Model (SDM) of the solar photovoltaic (PV) model, a non‐linear equation for the PV cell current is typically utilized. Then, the error between the estimated current and measured current is computed using the objective function called Root‐Mean‐Square‐Error (RMSE). In order to compute the PV cell current in SDM, an iterative method built on the Lambert‐W function is presented in this study. Along with the Lamber‐W function, an optimization algorithm called Weighted Velocity‐Guided Grey Wolf Optimizer (WVGGWO) is used to identify the unknown lumped parameters of SDM of the cell and the module. The proposed WVGGWO is an updated version of the original Grey Wolf Optimizer (GWO). The position update of the GWO has been modified, and the weightage has been provided for the wolf hierarchy. Additionally, by emphasizing the lengthening of each leading wolf's steps towards the others in the earlier search while emphasizing the shortening of the steps while reaching the later iterations, WVGGWO improves both the exploration and exploitation of the original GWO. Four case studies are considered for testing the validity of the proposed algorithm along with the Lambert‐W function. The performance of the proposed approach is compared with seven other well‐known algorithms. The results demonstrate that the suggested approach produces better outcomes than many optimization algorithms.
AimThe study aimed to determine the time to recovery of diabetic patients who have been treated in the hospital under follow-up. Subject and MethodsA retrospective cohort study design was carried out. The fast blood glucose level of diabetic patients who are under follow-up in the hospital was measured from 2016 to 2020. One thousand seven hundred diabetic patients were included in the study. Kaplan-Meier, Log-rank test, global test, Schoenfeld residuals, and Cox-PH model were used for statistical analysis.ResultsOut of the total of 1278 patients, 27.4% were censored (withdrawal from follow-up) and 72.6% recovered from the diabetic disease. For sex, the expected hazard is 1.322 times higher in males than female diabetic patients or there is a 32.2% increase in the expected hazard in males relative to female diabetic patients. For Spdrt, The expected hazard is 1.164 times higher in the patients who had taken leute than diabetic patients who took doanied. For regimen, the expected hazard is 1.495 times higher in the patients who had been treated by insulin agent only than diabetic patients who were treated by oral agents only ConclusionThe intensive-therapy regimen, Spdrt, and gender differences were statistically significant and critically contribute to the survival time to recovery of diabetic patients.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.