Railways speedily transport many people and goods nationwide, so railway accidents can pose immense damage. However, the infrastructure of railways is so complex that its maintenance is challenging and expensive. Therefore, using artificial intelligence for railway safety has attracted many researchers. This paper examines artificial intelligence applications for railway safety, mainly focusing on deep learning approaches. This paper first introduces deep learning methods widely used for railway safety. Then, we investigated and classified earlier studies into four representative application areas: (1) railway infrastructure (catenary, surface, components, and geometry), (2) train body and bogie (door, wheel, suspension, bearing, etc.), (3) operation (railway detection, railroad trespassing, wind risk, train running safety, etc.), and (4) station (air quality control, accident prevention, etc.). We present fundamental problems and popular approaches for each application area. Finally, based on the literature reviews, we discuss the opportunities and challenges of artificial intelligence for railway safety.

Background: Early morning behaviors between waking up and beginning daily work can develop into productive habits. However, sleep inertia limits the level of human ability immediately after waking, lowering a person’s motivation and available time for productive morning behavior.Objective: This study explores a design for morning behavior change using a wake-up task, a simple assignment the user needs to finish before alarm dismissal. Specifically, we set two research objectives: (1) exploring key factors that relate to morning behavior performance, including the use of wake-up tasks in an alarm app and (2) understanding the general practice of affecting morning behavior change by implementing wake-up tasks.Methods: We designed and implemented an apparatus that provides wake-up task alarms and facilities for squat exercises. We recruited 36 participants to perform squat exercises in the early morning using the wake-up tasks for 2 weeks. First, we conducted a generalized estimating equation (GEE) analysis for the first research objective. Next, we conducted a thematic analysis of the postsurvey answers to identify key themes about morning behavior change with the wake-up tasks for the second objective.Results: The use of wake-up tasks was significantly associated with both the completion of the target behavior (math task: P=.005; picture task: P<.001) and the elapsed time (picture task: P=.08); the time to alarm dismissal was significantly related to the elapsed time to completion (P<.001). Moreover, the theory of planned behavior (TPB) variables, common factors for behavior change, were significant, but their magnitudes and directions differed slightly from the other domains. Furthermore, the survey results reveal how the participants used the wake-up tasks and why they were effective for morning behavior performance.Conclusions: The results reveal the effectiveness of wake-up tasks in accomplishing the target morning behavior and address key factors for morning behavior change, such as (1) waking up on time, (2) escaping from sleep inertia, and (3) quickly starting the desired target behavior.

Background: Morning emotions can significantly affect daily wellness. While many studies have analyzed daily survey responses to identify factors influencing morning emotions, these methods require additional time and effort from individuals for emotional monitoring. Objective: This study aims to identify daily alarm usage patterns related to morning emotions. Methods: We recruited 373 users of the Alarmy app (DelightRoom) in the United States and South Korea and surveyed their demographics and usual behaviors related to morning emotions. Participants described their morning emotions over a 2-week period, during which we collected daily alarm app logs. We used a generalized estimating equation (GEE) method to identify factors affecting morning emotions. Results: The findings indicate that varied alarm usage is related to morning emotions. Alarm set time was positively associated with feelings of peacefulness and refreshment in the morning, while task-based alarms were related to nervousness. The time taken to deactivate the alarm after it rang was negatively correlated with happiness. In addition, usual behaviors and demographic factors were found to be related to morning emotions, consistent with previous studies. Conclusions: The study reveals that daily alarm usage is related to morning emotions, suggesting that daily alarm logs can supplement survey methods to facilitate daily emotion monitoring.

[This corrects the article DOI: 10.2196/39497.].

Pose embeddings provide a practical way to improve viewpoint invariance and occlusion robustness in 2D pose analysis by transforming sparse landmark coordinates into representations that preserve underlying 3D pose semantics. Existing approaches often rely on contrastive or variational objectives, which require careful pair construction, large batches, or additional training constraints. In this paper, we propose masked pose embedding (MPE), a transformer-based autoencoder trained with a single masked-reconstruction objective tailored to pose landmarks. MPE combines two key components: (1) view-angle canonicalization, in which randomly rotated 3D landmarks are projected to 2D and decoded toward the original canonical pose, and (2) landmark-level masking with masked-only supervision to improve robustness to partial visibility. We evaluate MPE in two complementary settings. First, on SARA and Human3.6M, we analyze whether distances in the learned embedding space correlate with ground-truth 3D pose distances under both controlled perturbations and under natural, unperturbed data. Second, on Yoga-82, we assess real image-based pose retrieval using YOLOv8x-pose landmarks under multiple synthetic occlusion patterns. The results show that MPE more faithfully preserves 3D pose semantics than recent single-frame and transformer-based representation baselines and achieves stronger retrieval accuracy among the compared methods under both full and partial landmark observations. These findings suggest that MPE provides an effective and training-stable solution for view- and occlusion-robust 2D pose embedding, while remaining highly parameter-efficient compared with recent single-frame and temporal representation baselines.