In this study, a Long Short-Term Memory (LSTM) model with extra variables such as weather conditions and school days was developed within a multi-scale framework in order to forecast passenger flow in both bus and rail systems, covering both regional and route-level analyses. In addition, the performance of the LSTM model was compared against three separate deep learning models. Among these, the Nonlinear Autoregressive Network with Exogenous Inputs (NARX) time series model produced the lowest error values, achieving a high level of accuracy. While no considerable changes were observed in regional rail passenger flow as a result of the inclusion of weather-related variables, a 2.2% drop in the RMSE value was achieved in bus passenger flow at the regional level; however, this improvement remains relatively modest. In contrast, at the route level, RMSE values declined by 2.4% for rail and 3.69% for bus routes. These findings reveal that the inclusion of weather-related variables significantly improves the prediction of bus passenger flow, underlining the benefits of integrating such data into forecasting models. Furthermore, the findings of this study analytically support transportation planners in making more informed, data-driven decisions regarding scheduling and capacity management.

Short-term wind power forecasting supports grid stability despite wind’s intermittency, but deep learning (DL) models often require excessive computation, hindering real-time deployment. This study compares three classical regression models, linear regression (LR), k-nearest neighbors (KNNs), and support vector regression (SVR) using 10-minute SCADA data. The data retained raw variability, with only negative power values set to zero. Hyperparameters of KNN and SVR were optimized using GridSearchCV with time series cross-validation. Optimized SVR achieved superior performance (MAE = 183.93 kW, RMSE = 387.64 kW, R ² = 0.91, MAPE = 144.88%, MdAE = 84.44 kW), outperforming KNN (R ² = 0.79) and LR (R ² = 0.91 but higher errors). Scatter plots and residuals highlighted SVR’s ability to handle nonlinearities. Results suggest that SVR is a viable candidate SVR for low-resource, real-time forecasting in renewables.

Architectures for building extraction are usually compared under different training and inference set-ups, which makes the reported performance figures difficult to compare directly. This study evaluates three convolutional segmentation architectures (U-Net, U-Net++, DeepLabV3+) and one transformer-based architecture (SegFormer) on the aerial orthophoto subset of the WHU Building Dataset under a single training and inference protocol. Fixing the encoder to ResNet-34 in the convolutional models separates the effect of decoder design from backbone capacity. U Net++ achieved the highest performance at 89.04% IoU and 94.20% F1, and the difference between the models remained within a narrow range of 2.42 points on the standard IoU metric. On the boundary IoU metric, which measures overlap only within the edge band, the same spread rose to 9.26 points, roughly 3.8 times as wide. Reporting standard IoU alone therefore systematically understates the difference between architectures. An inference protocol combining an exponential moving average, eight-way test-time augmentation and a decision threshold tuned on the validation set yielded gains of 0.47 to 0.69 IoU points without changing the ranking of the models; that magnitude corresponds to roughly a quarter of the total spread attributable to architecture. On the cost side, parameter count proved a misleading indicator: a 6.7% difference in parameters between U-Net++ and U-Net corresponded to a 135.2% difference in multiply accumulate operations. The results show that model selection in building extraction cannot rest on a single accuracy figure, and that boundary-based metrics should govern the choice in applications where the position of the boundary is critical.

Background/Objectives: Dental caries is one of the most common oral diseases worldwide, and early diagnosis is essential for effective treatment. However, detecting carious lesions in panoramic radiographs is challenging because of low image contrast, anatomical complexity, and overlapping structures. This study aimed to develop an improved object detection framework for dental caries localization in panoramic radiographs. Methods: A modified YOLOv8 architecture was developed by integrating ConvNeXtV2 blocks into the Cross-Stage Partial (C2f) modules to enhance feature extraction and gradient propagation. To improve detection performance and reduce overfitting, key training hyperparameters were optimized using a genetic algorithm. The proposed model was evaluated on a dataset of 474 panoramic dental radiographs annotated by experts using bounding boxes. Results: The optimized model achieved a box precision of 78.4%, a recall of 53.6%, and a mean average precision at 50% intersection-over-union threshold (AP50) of 62.6%. Compared with the baseline YOLOv8 model, the proposed approach improved precision and AP50. Visual and quantitative analyses demonstrated that the ConvNeXtV2-enhanced architecture enabled more accurate localization of carious regions. Conclusions: The results indicate that combining ConvNeXtV2-based architectural enhancement with genetic algorithm-based hyperparameter optimization is an effective strategy for dental caries localization in panoramic radiographs. Although recall remains a limitation, the proposed framework shows potential as a computer-aided diagnostic tool for supporting clinical caries assessment.

A considerable portion of worldwide energy usage and carbon emissions comes from buildings. Accurate short-term electricity load forecasting is critical to optimize building energy management systems. In this study, the PatchTST model was evaluated for short-term electricity load forecasting at the building scale. This forecasting problem was addressed using 50 buildings from the public Building Data Genome Project 2 dataset. Experiments were conducted using a strict temporal split strategy, where models generated 24-hour forward predictions from 336 hours of historical data. Temporal features, including hour, day, month, and weekend indicators, were integrated into the PatchTST model. The model was compared against Persistence, Long Short-Term Memory (LSTM), and DLinear baselines. On the test set, the PatchTST model outperformed all baselines, achieving a mean absolute error (MAE) of 7.24 kWh, a root mean square error (RMSE) of 13.85 kWh, an R² of 0.9894, and a symmetric mean absolute percentage error (MAPE) of 9.42%. Per-horizon analyses showed that PatchTST maintained stable performance across the forecast horizon. A building-level Wilcoxon signed-rank test indicated that the superiority of PatchTST over all baselines was statistically significant (p < 0.001) with large effect sizes These findings suggest that patch-based Transformer architectures provide high accuracy, statistical reliability for short-term electricity load forecasting.