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The importance of sustainability in architecture currently necessitates the integration of innovative teaching strategies on the subject into architecture programs. This study aims to introduce and examine peer learning pedagogy by peer tutoring to educate architecture students in sustainable design.

Based on class assignments proposed in two different architecture sustainability-focused courses in the second and fourth years of the Bachelor of Science in architecture program, a total of 103 students assessed the proposed peer learning experience and its impact on their sustainability mindsets and education. Subjective surveys for evaluating the peer learning experience were designed and delivered at different stages of the course sequences. A total of 502 survey responses were obtained in the study.

The qualitative and quantitative data analysis confirms that the proposed peer learning by peer tutoring increased students’ knowledge, motivation and commitment to sustainable design. In addition, participants became more confident in applying sustainable design skills and their academic grades improved more than 25% compared to previous courses using traditional teaching methods.

Traditional architecture education has long been criticized for their pedagogical methodologies based primarily on passive learning. Recently, these programs have begun to prepare students to become active learners and communicators in collaborative and multidisciplinary environments. A mixed-method approach of combining pre-/post-experience surveys and analysis of final grades was used to determine the level of success and the quantifiable behavior change delivered by students involved in this peer learning experience.

Exposure to indoor air pollutants poses a significant health risk, contributing to various ailments such as respiratory and cardiovascular diseases. These unhealthy consequences are specifically alarming for athletes during exercise due to their higher respiratory rate. Therefore, studying, predicting and curtailing exposure to indoor air contaminants during athletic activities is essential for fitness facilities. The objective of this study is to develop a neural network model designed for predicting optimal (in terms of health) occupancy intervals using monitored indoor air quality (IAQ) data.

This research study presents an innovative approach employing a long short-term memory (LSTM) recurrent neural network (RNN) to determine optimal occupancy intervals for ensuring the safety and well-being of occupants. The dataset was collected over a 3-month monitoring campaign, encompassing 15 meteorological and indoor environmental parameters monitored. All the parameters were monitored in 5-min intervals, resulting in a total of 77,520 data points. The dataset collection parameters included the building’s ventilation methods as well as the level of occupancy. Initial preprocessing involved computing the correlation matrix and identifying highly correlated variables to serve as inputs for the LSTM network model.

The findings underscore the efficacy of the proposed artificial intelligence model in forecasting indoor conditions, yielding highly specific predicted time slots. Using the training dataset and established threshold values, the model effectively identifies benign periods for occupancy. Validation of the predicted time slots is conducted utilizing features chosen from the correlation matrix and their corresponding standard ranges. Essentially, this process determines the ratio of recommended to non-recommended timing intervals.

Humans do not have the capacity to process this data and make such a relevant decision, though the complexity of the parameters of IAQ imposes significant barriers to human decision-making, artificial intelligence and machine learning systems, which are different. Present research utilizing multilayer perceptron (MLP) and LSTM algorithms for evaluating indoor air pollution levels lacks the capability to predict specific time slots. This study aims to fill this gap in evaluation methodologies. Therefore, the utilized LSTM-RNN model can provide a day-ahead prediction of indoor air pollutants, making its competency far beyond the human being’s and regular sensors' capacities.

This paper aims to present experimental results related to the performance of cactus mucilage (CM) and brown seaweed extracts (SEs) to inhibit reinforcing steel bar (rebar) corrosion in saturated calcium hydroxide alkaline solutions (pH = 12.5).

Electrochemical cells were prepared using CM solutions at 0.5, 1 and 1.38 per cent concentration (w/v), SE solutions at 0.5, 1, 1.38, 2 and 3 per cent concentration (w/v), sodium alginate at 1 per cent concentration (w/v) and calcium nitrite at 11.3 per cent (v/v). Each cell contained six deformed reinforcing steel bars of 9.5 mm nominal diameter. The experiments were performed at 23 ± 2°C in two stages. The first stage was aimed at stabilizing the rebar until passivation was reached. The second stage included adding NaCl in six steps from 0.5 to 16 g/L. Half-cell potential, linear polarization resistance and electrochemical impedance spectroscopy measurements were monitored during both stages.

The electrochemical test results indicated that both additions reduce the corrosion rate of rebars and pitting in an alkaline media with chloride ions (16 g/L NaCl). Electrochemical impedance spectroscopy results for rebars in natural-added solutions showed higher charge transfer resistance and double layer capacitance values, indicative of the formation of a second interface between the rebar and the electrolyte.

The information obtained was for alkaline solutions only. Further investigation is performed using concrete as the alkaline electrolyte.

CM and SE may be suitable low-cost corrosion inhibitors for steel in concrete.

The use of botanical or algae products for this application will encourage people to consider its production for this particular application. Also, the possible harvest in an environmental friendly way will diminish in the future the use of biohazards and toxic inhibitors.

This investigation is a continuation of a one presented in 2007, which uses only nopal mucilage. This new investigation corroborates what was concluded in the early investigation and incorporates a new natural by product, algae, as a possible corrosion inhibitor product.