Mohammad Reza Sabour

The study conducted in Kerman, Iran, examines healthcare waste generation rates in 15 hospitals, encompassing urban and rural settings, using machine learning models. The research emphasizes the significance of specific hospital metrics in predicting waste quantities and stresses the importance of accurate prediction techniques for effective waste management. Machine learning, a field that merges computer science with statistical methods, is highlighted for its potential to aid data-driven decision-making and predictive analytics in various industries. The study employs Multi Linear Regression (MLR) and Artificial Neural Networks (ANN) to analyze hospital waste generation, utilizing advanced methodologies to improve waste management practices. Evaluation metrics like Mean Squared Error, Mean Absolute Error, and coefficient of determination are used to assess model performance, with optimal architectures for the models determined through experimentation and parameter tuning to demonstrate their predictive capabilities and performance metrics.

This study presents an integrated assessment of water quality in the Haraz Dam reservoir, considering both external pollutant inflows and internal contributions from a nearby unregulated landfill. The CE‐QUAL‐W2 hydrodynamic model was employed to simulate monthly variations of nitrate, phosphate, ammonium, dissolved oxygen, and pH across four water withdrawal levels, capturing the effects of thermal stratification and anaerobic conditions. Nitrate and phosphate were identified as the main drivers of water quality degradation, with the baseline Iran Water Quality Index for Surface Water Resources‐Conventional Parameters (IRWQIsc) values classifying the reservoir as “relatively poor.” Four management scenarios were evaluated: Scenario 1, reducing nitrate and phosphate inflows by 50%, improved IRWQIsc by 9.5 points, and lowering nitrate and phosphate concentrations by 46.8% and 41.6%, respectively, and shifting water quality to “average.” Scenario 2, maintaining current pollutant loads over 5 years, predicted IRWQIsc values consistently below 40 at lower withdrawal levels, indicating ongoing poor water quality. Scenario 3, applying a sustained 50% reduction over 5 years, further improved IRWQIsc by 10 points, with nitrate and phosphate reductions of 52.7% and 51.6%. Scenario 4, removing the nitrate‐contaminated leachate spring, reduced nitrate in the agricultural withdrawal level by only 4.9%, highlighting the limited impact of intrareservoir interventions alone. These results demonstrate that watershed‐level management of pollutant inflows is essential for achieving long‐term improvements in reservoir water quality, offering a replicable framework for hydrodynamic and water quality modeling in dam reservoirs.

Numerous environmental issues have been addressed as a consequence of rapidly increasing global energy consumption. Building energy consumption has risen considerably in the last few decades. In recent years, the zero energy building (ZEB) concept has attained worldwide attention and is now commonly identified as the future criteria for building design. However, there is a lack of uniform definition in this field. This study combines bibliometric and visual analysis of Scopus database publications from 1977 to 2022 to investigate ZEB-related studies. Five topical clusters formed as a result of evaluating the co-occurrence network of the keywords. Finally, the most popular research trends were determined by investigating the overlay network. The scientific knowledge concerning zero energy buildings has been mapped by employing the results of this paper. In addition, they have been utilized to highlight future opportunities and gaps and illustrate the connections between various subject areas.

Detergents are one of the most serious environmental issues. This challenge, on the other hand, has been around for a long time, with articles on it dating back more than a century. Furthermore, the number of researchers in this field has increased as a result of the emergence of the Coronavirus in early 2020. The reason appears to be that, according to the structure of the coronavirus, detergents are capable of killing the virus, and it can be stated that using washing solutions in conjunction with a mask is one of the most important strategies for preventing corona spread. The purpose of this research is to create a bibliometric and review article that can be viewed at a glance from 2000 to 2020 in order to understand the trend of studies in this field. Despite the scarcity of research in this field, an attempt has been made to take a professional look at it. According to the findings, there has been a significant increase in the number of detergent-related publications over the last 20 years, indicating strong research growth trends. According to the subject category study, the most common subject categories were biochemistry, genetics, and molecular biology. "Journal of Biological Chemistry" is the most productive journal, followed by "Journal of Dairy Science," "Animal Feed Science and Technology," and "Revista Brasileira De Zootecnia." The United States is the largest contributor to the total number of publications, followed by Brazil and China. Detmann, E. from Brazil ranks first among the authors by a significant margin (nearly two times) over the other authors in the field of detergent. Furthermore, keyword clustering analysis was used to identify the pioneer countries, and it revealed that the volume of landfill leachate-related publications increased significantly during the study period.