Prabhjot Singh Chani

While greenery is often associated with cognitive restoration, this study demonstrates that it alone may not be sufficient to optimize neurocognitive recovery in academic environments. Using ecologically validated virtual reality (VR) environments developed through artificial intelligence (AI)–driven generative modeling (Skybox AI Model 3.1; procedural and diffusion-based rendering) and electroencephalography (EEG)–based neurophysiological assessments, we explore how the composition of built and natural elements alongside higher-order spatial constructs like prospect, refuge, and novelty influences restoration. Cognitive restoration was indexed using normalized alpha brainwave (NAB) activity, a relative measure of alpha power commonly associated with relaxed wakefulness and mental recovery. Stepwise multiple regression models were employed to test two hypotheses. Model 1 examined the influence of macro built environment variables (BEVs), operationalized as visual percentage compositions of buildings, greenery, water, and visible sky within 360° panoramic views on NAB activity. These BEVs were manipulated in VR individually and in combination, and their intensification was quantified using visual area coverage. The model explained 64.5% of the variance in NAB (adjusted R ² = 0.645, p < 0.001), with greenery (standardized β = 9.009) contributing most positively, although its effects plateaued with intensification. Built structures (standardized β = 8.738), typically considered detrimental, were found to enhance NAB when spatially balanced with sky openness ( β = 4.193), water features ( β = 3.641), and environmental variation, suggesting a design-dependent restorative effect. Model 2 assessed the role of higher-order spatial constructs: prospect, refuge, environmental novelty, and overall environmental category quantified through expert evaluations of perceptual cues, spatial enclosure, and uncommon design patterns. This model accounted for 67.7% of the variance in NAB (adjusted R ² = 0.677, p < 0.001), with novelty (standardized β = 0.517) outweighing the influence of individual physical variables. Rather than offering a binary green versus built narrative, the study proposes a predictive framework for restorative academic design based on an optimized interplay of physical and perceptual variables. By integrating EEG data with AI-driven spatial simulations, this research contributes to evidence-based campus planning, suggesting that cognitive restoration emerges not solely from greenery, but from a compositional harmony of built and natural features, balanced prospect–refuge dynamics, and perceptual novelty.

Enhancing the energy efficiency of urban housing is crucial for improving energy access in Ethiopia, where a persistent gap exists between energy demand and supply. The nationwide Integrated Housing Development Program (IHDP), implemented across diverse climatic zones with limited attention to local context and building energy performance, has inadvertently produced thermally uncomfortable and energy-inefficient urban dwellings. This study employs a scenario-based intelligent energy simulation framework to address this inefficiency by identifying energy-saving opportunities in urban housing, pioneering the smart meter data-driven ClimateStudio-EnergyPlus workflow-validated through machine-readable consumption analytics (R$^2$ = 0.79 in Addis Ababa; 0.64 in Adama) and on-site surveys across temperate highland Addis Ababa and hot-arid Adama. Appliance usage inventory reveals cooking dominance, accounting for 60-62.9% of total loads. Adama's 20% household fan-based thermal coping behavior signals a latent air-conditioning demand projected to surge 31% above baseline in arid lowlands versus 6% in temperate zones as affordability erodes overheating tolerance. Monthly consumption heatmaps reveal unit-size and seasonal interaction effects diverging sharply between cities, with Adama's studio units peaking at approximately 191~kWh/month in the dry season against a stable 150-190~kWh/month range in Addis Ababa. Envelope diagnostics expose a systemic performance gap, wall U-values of 1.61~W/m$^2$K and glazing SHGC of 0.86-0.87 exceeding best-practice targets by three- to fourfold, with Adama's larger glazed area compounding solar heat gain risk in the climate zone least suited to absorb it. Targeting dominant local cooking appliance upgrades yields immediate operational savings of 18-18.9%. Furthermore, these empirical benchmarks recommend region-tailored building envelopes in conjunction with Minimum Energy Performance Standards (MEPS) for cooking appliances, establishing foundational performance data for Ethiopia's unimplemented building codes and informing climate-affordability demand forecasting to achieve policy-aligned efficiency gains.

Human-centric intelligent environments require reliable sensing mechanisms capable of identifying occupant cognitive states in real time. In adaptive building systems, differentiating between cognitive stress and boredom is particularly important, as these states require contrasting environmental responses. This study presents a systematic cross-modal comparison of facial infrared thermography (fITI) and eye-tracking features for contactless detection of these states. Eight participants completed stress (Montreal Imaging Stress Test) and boredom (sustained visual monitoring) tasks under controlled lighting conditions (150 lux artificial lighting and 300 lux daylight), while synchronized thermal and oculomotor data were recorded. Linear mixed-effects models were applied to 320 observations per modality. Results revealed three key findings. First, spatial thermal heterogeneity within facial regions of interest (ROI), quantified through pixel standard deviation, outperformed conventional mean temperature measures. The strongest thermal effect was observed in the Lower Left Cheek (|t| = 7.92), reflecting asynchronous discharge dynamics of facial arteriovenous anastomoses under sympathetic activation. A lateralized hemispheric asymmetry (|t| = 6.93) further indicated inter-hemispheric thermal divergence between states. Second, eye-tracking measures demonstrated high discriminative capability, with nine out of ten features reaching significance and a 7.2-fold median effect-size advantage over thermal metrics; baseline-corrected pupil diameter showed the strongest separation (|t| = 46.06) with immediate and stable differentiation between conditions. In contrast, thermal responses exhibited dual temporal dynamics, with immediate separation in heterogeneity features and progressive divergence in periorbital maxima over 5–10 minutes. Third, variance decomposition identified periorbital thermal regions (ICC = 0.06–0.09) as calibration-free indicators suitable for practical sensing systems, complementing low-ICC oculomotor features. Subjective validation through NASA-TLX (d = 1.63; per-participant ρ = 0.63) confirmed that physiological markers tracked experienced cognitive states rather than task labels. The resulting feature set is compatible with commercially available sensing hardware and provides a foundation for integrating human-centric physiological monitoring into intelligent adaptive environments.