Ultrahigh-porosity Ranunculus-like MgO adsorbent coupled with predictive deep belief networks: A transformative method for phosphorus treatment.

Phosphorus is a nonrenewable material with a finite supply on Earth; however, due to the rapid growth of the manufacturing industry, phosphorus contamination has become a global concern. Therefore, this study highlights the remarkable potential of ranunculus-like MgO (MO4-MO6) as superior adsorbents for phosphate removal and recovery. Furthermore, MO6 stands out with an impressive adsorption capacity of 596.88 mg/g and a high efficacy across a wide pH range (2-10) under varying coexisting ion concentrations. MO6 outperforms the top current adsorbents for phosphate removal. The process follows Pseudo-second-order and Langmuir models, indicating chemical interactions between the phosphate species and homogeneous MO6 monolayer. MO6 maintains 80 % removal and 96 % recovery after five cycles and adheres to the WHO and EUWFD regulations for residual elements in water. FT-IR and XPS analyses further reveal the underlying mechanisms, including ion exchange, electrostatic, and acid-base interactions. Ten machine learning (ML) models were applied to simultaneously predict multi-criteria (sorption capacity, removal efficiency, final pH, and Mg leakage) affected by 15 diverse environmental conditions. Traditional ML models and deep neural networks have poor accuracy, particularly for removal efficiency. However, a breakthrough was achieved by the developed deep belief network (DBN) with unparalleled performance (MAE = 1.3289, RMSE = 5.2552, R2 = 0.9926) across all output features, surpassing all current studies using thousands of data points for only one output factor. These captivating MO6 and DBN models also have immense potential for effectively applying in the real water test with error < 5 %, opening immense horizons for transformative methods, particularly in phosphate removal and recovery.

Dataset on the effect of gamification elements on learning effectiveness among Vietnamese students.

The enhancement of learning effectiveness among university and graduate students is a continual and perplexing question for educational institutions. With the advancement of technology, educators are exploring and implementing diverse learning methods. In this current climate, gamification has emerged as an innovative and popular approach to education. Therefore, it is crucial to thoroughly examine the impact gamification has on the learning effectiveness of university and graduate students. The data also includes demographics of the participants. The questionnaire-based data was collected from previous studies with university and graduate students in Vietnam. The study used Excel and SmartPls 4.0 software to analyze data.. This research can serve as a resource for educational managers, lecturers, and universities to comprehend the effect of gamification on learning effectiveness. It can also aid researchers in the field to explore further the impact of gamification elements and their mediating roles, ultimately enhancing the learning effectiveness of university and graduate students.