Today, HVAC (heating, ventilation, and air conditioning) systems have become an integral part of modern buildings and are designed to provide comfortable indoor environments while conserving energy and reducing carbon emissions. With advancement in technology, HVAC systems have a variety of sensors that are used to detect the occupants within a controlled environment. Advancements in computer control systems and the use of smart technology have made HVAC systems even more sophisticated, allowing for approximate temperature control and energy management. This paper will review the historical development of technology and the current state of HVAC systems. With the proper data, development of artificial intelligence models can, in theory, improve the overall optimization and reduce energy consumption This paper will provide a review of HVAC history and the key concepts around the usefulness of using AI from previous research conducted in this field of study.

The control of thermostats of a heating, ventilation, and air-conditioning (HVAC) system installed in commercial and residential buildings remains a pertinent problem in building energy efficiency and thermal comfort research. The ability to determine the number of people at a particular time in an area is imperative for energy efficiency in order to condition only occupied regions and thermally deficient regions. In this study of the best features comparison for detecting the number of people in an area, feature extraction techniques including wavelet scattering, wavelet decomposition, grey-level co-occurrence matrix (GLCM) and feature maps convolution neural network (CNN) layers were explored using thermal camera imagery. Specifically, the pretrained CNN networks explored are the deep residual (Resnet-50) and visual geometry group (VGG-16) networks. The discriminating potential of Haar, Daubechies and Symlets wavelet statistics on different distributions of data were investigated. The performance of VGG-16 and ResNet-50 in an end-to-end manner utilizing transfer learning approach was investigated. Experimental results showed the classification and regression trees (CART) model trained on only GLCM and Haar wavelet statistic features, individually achieved accuracies of approximately 80% and 84%, respectively, in the detection problem. Moreover, k-nearest neighbors (KNN) trained on the combined features of GLCM and Haar wavelet statistics achieved an accuracy of approximately 86%. In addition, the performance accuracy of the multi classification support vector machine (SVM) trained on deep features obtained from layers of pretrained ResNet-50 and VGG-16 was between 96% and 97%. Furthermore, ResNet-50 transfer learning outperformed the VGG-16 transfer learning model for occupancy detection using thermal imagery. Overall, the SVM model trained on features extracted from wavelet scattering emerged as the best performing classifier with an accuracy of 100%. A principal component analysis (PCA) on the wavelet scattering features proved that the first twenty (20) principal components achieved a similar accuracy level instead of training on the whole feature set to reduce the execution time. The occupancy detection models can be integrated into HVAC control systems for energy efficiency and security systems, and aid in the distribution of resources to people in an area.

This study examined differences in reported campus criminality on selected community college campuses in the years between 2005 and 2016 representing the years preceding and succeeding the implementation of Mississippi’s 2011 concealed carry legislation. Each campus included in this study was a public-funded community college in Mississippi. Using a significance level of 0.05 and an analysis of variance approach, the hypothesis testing showed five statistically significant findings reflecting the cases of Copiah-Lincoln, East Central, Jones County, Mississippi Delta, and Northeast Mississippi Community Colleges. Regarding these outcomes, an analysis of the means showed overall lowered crime reports.

Phase change materials (PCMs) offer potential for passive thermal regulation in building envelopes through latent heat storage; however, their effectiveness remains strongly climate-dependent, and configurations optimized for one region often underperform in others. Existing evaluation approaches rely largely on location-specific simulations or surrogate models with limited climate transferability. This study develops a physics-informed, climate-aware machine-learning framework to assess PCM-integrated wall assemblies across diverse climates. A structured dataset of 720 EnergyPlus simulations was generated across nine PCM materials, ten ASHRAE climate zones, two placement configurations, and four thickness levels using automated model generation and batch simulation through Eppy-based workflows. Ensemble-based models (XGBoost, LightGBM, CatBoost, Random Forest) were trained under climate-grouped validation to predict total annual energy consumption, peak cooling demand, and peak heating demand. The models achieved high predictive accuracy for total annual energy use (R² ≈ 0.98–0.99) and peak cooling demand (R² ≈ 0.93–0.96), outperforming statistical, climate-only, and PCM-agnostic baselines. In contrast, peak heating demand showed low predictability (R² ≤ 0.26), indicating limited sensitivity to PCM parameters under the studied configuration. These results demonstrate that climate-aware validation enables defensible cross-climate PCM assessment, supporting energy demand reduction and sustainable envelope design decisions aligned with global building decarbonization goals.

Additive manufacturing is increasingly used in construction, yet reliable quality assurance for three-dimensional-printed concrete elements remains a major challenge. Existing digital defect-detection methods, particularly voxel-based and mesh-based approaches, are often evaluated separately, which limits understanding of their relative capabilities for construction-scale inspection. This study establishes a controlled comparison of the two representations using identical scan-to-design data, consistent preprocessing, and unified defect thresholding. A voxel pipeline employing signed distance fields and a three-dimensional convolutional neural network, and a mesh pipeline using triangular surface reconstruction, geometric surface descriptors, and MeshCNN, were applied to structured-light scans of printed clay wall segments containing intentional voids, material buildup, and layer-height inconsistencies. Across common performance metrics, the voxel-based method achieved a recall of 95% for spatially coherent, volumetric-consistent void-related anomalies inferred from surface geometry, reflecting improved aggregation of distributed deviations, while the mesh-based method attained a mean surface defect localization error of 0.32 mm with a substantially lower computational cost in runtime and memory. These results clarify representation-dependent trade-offs and provide guidance for selecting appropriate inspection pipelines in extrusion-based construction. The findings establish a controlled, construction-oriented comparative framework for digital defect detection and support more efficient, reliable, and scalable quality-assurance workflows for sustainable additive manufacturing.

The SETSCI Conference Proceedings is an Open Access publication created for the storage and indexing of proceedings | Understanding pedestrian perception is essential for designing walkable and inclusive urban environments. This study employs real-world eye-tracking to investigate how pedestrians visually engage with urban streetscapes during everyday navigation. Using wearable eye-tracking technology, the research captures gaze behavior and fixation patterns across various streetscape elements including sidewalks, crosswalks, greenery, and building facades. The analysis reveals that features associated with spatial clarity and safety—such as curbs and well-defined pedestrian paths—receive prolonged visual attention. Natural elements like vegetation also play a significant role in shaping visual comfort and environmental appeal. Gaze data segmented by Areas of Interest (AOIs) shows a clear hierarchy of visual focus, suggesting that spatial composition and surface detail directly impact cognitive processing and navigation. Additionally, gender-based differences in attention strategies indicate the need for inclusive design that supports diverse user behaviors. By translating subconscious visual behavior into actionable insights, this research contributes to the advancement of evidence-based urban design practices. The results highlight how realtime perceptual data can inform the creation of more intuitive, comfortable, and engaging city spaces.