The COVID-19 pandemic has posed significant challenges to all facets of education. As students are slowly repopulating university campuses after lockdowns and online learning, universities are looking into ways to ensure social distancing can be maintained in learning spaces and capitalize on the benefits of online-learning modalities without compromising educational quality. One option that has gained attention is hybrid or dual-delivery learning. In this model, some students are present in classrooms, while others join the class through online platforms. However, most university classrooms are not designed to deliver the voice of instructors and classroom students to online platforms. This change in modality requires universities to invest in infrastructure and technology. This research studies the optimum setup for dual-delivery classrooms, investigating a range of infrastructural and technology improvements that can be made to traditional classrooms to help optimize the perceived sound quality for remote learners. The investigation entails a qualitative study to assess the improvement in perceived sound quality (clarity, ability to recognize words, and perceived echo) for remote students vis-a-vis the improvement in the room (such as sound-absorbing padding or carpets) and its technology (such as ceiling and lapel microphones). Specifically, it investigates the degrees of room and the technology improvements needed to ensure that the voice of instructors and the classroom students’ interactions, such as questions and discussions, are heard and comprehended by remote students. We collected responses for nine experiment conditions through 39 tests addressing both instruction and students’ interactions. We formulate a matrix of recommendations for higher education institutions to follow. The suggestions proposed also have other environmental benefits beyond sound quality.

Computational fluid dynamics (CFD) is a powerful tool for modeling turbulent flow and is commonly used for urban microclimate simulations. However, traditional CFD methods are computationally intensive, requiring substantial hardware resources for high-fidelity simulations. Deep learning (DL) models are becoming popular as efficient alternatives, requiring less computational resources to model complex non-linear interactions in fluid flow simulations. A major drawback of DL models is that they are prone to error accumulation in long-term temporal predictions, often compromising their accuracy and reliability. To address this shortcoming, this study investigates the use of a denoising diffusion probabilistic model (DDPM) as a novel post-processing technique to mitigate error propagation in DL models' sequential predictions. To address this, we employ convolutional autoencoder (CAE) and U-Net architectures to predict airflow dynamics around a cubic structure. The DDPM is then applied to the model's predictions, refining the reconstructed flow fields to better align with high-fidelity statistical results from large-eddy simulations. Results demonstrate that, although deep learning models provide significant computational advantages over traditional numerical solvers, they are susceptible to error accumulation in sequential predictions; however, utilizing DDPM as a post-processing step enhances the accuracy of DL models by up to 65% while maintaining a three times speedup compared to traditional numerical solvers. These findings highlight the potential of integrating denoising diffusion probabilistic models as a transformative approach to improving the reliability and accuracy of deep learning-based urban microclimate simulations, paving the way for more efficient and scalable fluid dynamics modeling.

In residential buildings, air infiltration impacts energy, indoor air quality, and thermal comfort considerably. While air infiltration in residential buildings has been the focus of many studies, most published field-testing results pertain to developed countries, with little or no data on air infiltration in developing nations. This paper presents the results of one of the first field investigations into envelope infiltration in the residential buildings of the hot-arid climatic area of Egypt. To analyze the air permeability of the building envelope, the fan pressurization method, often known as the blower door test (BDT), is used, following ISO 9772. The study focuses on 20 residential dwellings built with heavy construction materials and subjects them to extensive characterization and testing. The average air leakage and the air permeability rate for the tested sample were 6.14 h⁻¹ and 17.3 m³/(h∙m²), respectively. However, significant variations in airtightness were observed across the dwelling, leading the team to test several building-related parameters statistically to study their impact on airtightness. Fenestration quality appeared to be a critical factor in determining air infiltration, showing a strong correlation with the air change and leakage. A further investigation underscored that the specific aperture factor and the fenestration quality can predict the infiltration rates to a large degree. Thus, we recommend further investigation of these characteristics in heavy construction material building. Finally, we strongly recommend that building codes in developing countries such as Egypt include minimum performance requirements for fenestration.

Between the synergies of environmental perception and technological advancement evolves the parallel world of the metaverse. Evolutionary virtuality intends to aid humanity in envisioning the threatened future of cities under environmental risks through tailored features. Traditionally, the sense of sight – which is the focus of virtual reality – has dominated the architectural practice. However, architects and urban designers have begun incorporating other senses into their work over the recent decade. The expanding understanding of the multimodal nature of the human mind that has evolved from cognitive neuroscience research has received little attention so far in the architecture field. This paper investigates the role of synthesized sensory experiences – such as visual, auditory, olfactory, gustatory, and thermal sensations – in designing revolutionary settings that aim to improve people’s interactions with their surrounding environments. A 15-minute experiment of an immersive experience in an office setting using virtual reality headsets is utilized to explore the role of multimodal sensory integration towards tolerance to the thermal environment. The findings revealed significant potential in using multiple senses – especially gustatory – to design thermally comfortable spaces. It is hoped that architectural design practice would progressively include our developing understanding of human senses and how they interact. This holistic approach ought to lead to the development of multisensory-inclusive workspaces that promote rather than hinder our social, cognitive, and emotional development.