This paper evaluates a VR (Virtual Reality) application aimed at enhancing the learning of Python collection data types and structures for electrical and electronic engineering students. By incorporating gamification and personalisation features, the application provides an immersive environment where students can interact with virtual representations of complex programming concepts. To further enhance interactivity and engagement, the application integrates a virtual assistant and example generator, developed using Meta Voice SDK (Software Development Kit) and wit.ai. These AI (Artificial Intelligence)-NLP (Natural Language Processing) tools create personalised learning paths and generate dynamic examples based on individual learning progress. A user study was conducted with a total of 48 participants. During the user study, participants were divided into two equal groups of 24, both wearing EEG (Electroencephalography) headsets: one group engaged with the VR application, while the other read the traditional booklet, allowing for the recording and analysis of attention and engagement levels. These measures of engagement and attention were then compared to those extracted from a benchmark cohort of students whose learning experience was through more traditional booklets. The results indicated a statistically significant improvement in understanding Python collections among VR users compared to their baseline scores, highlighting the benefits of interactive and tailored learning environments. Additionally, EEG data analysis showed that VR users exhibited higher average levels of attention and engagement compared to those using the paper-based method, demonstrating the effectiveness of immersive technologies in sustaining learner interest and focus, particularly in enhancing learning for software development.

Background: This study evaluated temporal changes in stage distribution, registry-recorded primary-site surgery, survival outcomes, and exploratory short-term mortality prediction among patients with early-onset testicular germ cell tumors, defined in this study as tumors diagnosed in patients aged 15–49 years, across the study periods. Materials and Methods: This retrospective cohort study used Incidence-SEER Research Data, 17 Registries, November 2025 Submission. Male patients aged 15–49 years diagnosed with malignant testicular germ cell tumors between 2018 and 2023 were included. Diagnosis periods were categorized as the pre-COVID-19 period (2018–2019), COVID-19 period (2020–2021), and 2022–2023 period. Logistic Regression was used to assess factors associated with regional or distant-stage disease. Overall survival was evaluated using Cox regression, while testicular cancer-specific death was additionally evaluated using Aalen–Johansen cumulative incidence estimates and Fine–Gray competing-risk regression. Follow-up was also restricted to 12 months in sensitivity analyses. Machine learning models were developed as an exploratory secondary analysis of 12-month all-cause mortality. Results: A total of 13,303 patients were included. Among patients with known stage, regional or distant-stage disease increased from 31.2% before the pandemic to 33.5% during the COVID-19 period and 34.4% during the 2022–2023 period. In adjusted analysis, COVID-19 (OR = 1.11, 95% CI: 1.02–1.22) and diagnoses during the 2022–2023 period (OR = 1.16, 95% CI: 1.06–1.28) were associated with higher odds of regional or distant-stage disease. In full follow-up analyses, diagnosis during the 2022–2023 period was associated with higher all-cause mortality (HR = 1.29, 95% CI: 1.01–1.65) and testicular cancer-specific death in both cause-specific Cox (HR = 1.49, 95% CI: 1.12–2.00) and Fine–Gray models (sHR = 1.45, 95% CI: 1.09–1.93). In 12-month analyses, the overall survival association was attenuated, whereas the association with testicular cancer-specific death remained significant in the Fine–Gray model (sHR = 1.46, 95% CI: 1.03–2.07). Conclusions: Early-onset testicular germ cell tumors diagnosed during the COVID-19 period and in 2022–2023 showed a modest shift toward regional or distant-stage presentation, while diagnosis in 2022–2023 was associated with higher testicular cancer-specific mortality in competing-risk analyses.

Visual impairment significantly limits independent mobility and environmental awareness in daily life. Existing assistive technologies often provide limited contextual understanding and rely on passive feedback, while many deep learning-based detection systems continuously announce all detected objects, increasing cognitive load in real-world use. To address these challenges, this study presents VisionAssist, an iOS-based mobile application that integrates real-time object detection, voice command interaction, and directional auditory feedback to support active, user-centered environmental perception. The system is implemented using Swift and SwiftUI and deployed on-device via Apple’s Core ML framework, ensuring energy efficiency and data privacy. VisionAssist enables users to verbally request specific objects and provides spatial auditory cues indicating object direction and confidence-based reliability. A comparative evaluation between YOLOv8 and YOLOv26 shows that YOLOv8 achieves higher detection accuracy and better usability due to more stable recognition, while YOLOv26 offers lower computational and energy costs but reduced consistency. Overall, YOLOv8 is preferred for interaction quality and trustworthiness, whereas YOLOv26 is more suitable for efficiency-focused use cases. Additionally, confidence-based filtering reduces unnecessary or ambiguous feedback while maintaining situational awareness. The results demonstrate that VisionAssist is a practical and effective assistive solution for improving independent navigation and environmental understanding for visually impaired individuals.

Background and Objectives: The COVID-19 pandemic disrupted cancer diagnosis and treatment pathways worldwide. Glioblastoma is an aggressive primary brain malignancy requiring timely multimodal care. This study evaluated survival outcomes among glioblastoma patients diagnosed before and during the COVID-19 pandemic and prepared a dataset for machine learning-based prediction of 12-month mortality. Materials and Methods: Patients aged ≥20 years diagnosed with glioblastoma between 2018 and 2021 were identified from the SEER database using ICD-O-3 histology codes 9440/3, 9441/3, and 9442/3. Patients were categorized as pre-COVID period (2018–2019) or COVID period (2020–2021). OS and CSS were evaluated using Kaplan–Meier curves, log-rank tests, and Cox regression models. Machine learning models predicted 12-month all-cause mortality using registry variables. Results: The final cohort included 9914 patients; 4819 were diagnosed pre-COVID and 5095 during COVID. Median OS was 11 months pre-COVID and 10 months during COVID; 12-month OS was 44.3% and 41.2%, respectively. Median CSS was 11 months in both periods; 12-month CSS was 46.9% and 44.1%, respectively. COVID-period diagnosis was modestly associated with poorer OS (adjusted HR 1.050, 95% CI 1.006–1.095, p = 0.025) and CSS (adjusted HR 1.048, 95% CI 1.003–1.095, p = 0.035). Machine learning models showed moderate discrimination for 12-month mortality prediction. Conclusions: Glioblastoma patients diagnosed during the COVID period had modestly poorer OS and CSS in conventional survival analyses; however, competing-risk analysis did not show a significant association with cancer-specific death. Registry-based machine learning models provided moderate 12-month mortality prediction, supporting their potential utility for population-level prognostic assessment.

Background/Objectives: The COVID-19 pandemic coincided with widespread disruption of cancer care. We examined changes in lung adenocarcinoma presentation and outcomes across the COVID-19 era and whether diagnosis-time information identifies patients at risk of early death. Methods: Adults with microscopically confirmed lung adenocarcinoma diagnosed during 2018–2023 were identified from the Surveillance, Epidemiology, and End Results 17 Registries database and grouped into three diagnostic periods. Logistic regression, time-varying Cox, and Fine-Gray models evaluated stage, metastasis, surgery, delay, and mortality. Four machine-learning algorithms using only diagnosis-time variables were developed (2018–2021; n = 87,281) and temporally validated (2022; n = 22,012). Results: Among 133,306 patients, COVID-19 versus pre-COVID-19 differences were significant but small in absolute terms: stage IV disease 47.9% versus 47.4% (adjusted odds ratio [OR], 1.03; 95% CI, 1.00–1.06), organ-specific metastasis 45.8% versus 44.4% (OR, 1.06; 1.04–1.09), and treatment delay exceeding 90 days 11.1% versus 10.2% (OR, 1.11; 1.06–1.17). Post-COVID-19 (2022–2023) diagnosis showed less advanced disease but substantially longer delay (14.5% versus 10.2%; OR, 1.45; 1.39–1.52). Gradient boosting achieved a 2022 ROC AUC of 0.840 (0.834–0.845); it flagged 46.8% of patients and correctly flagged 81.6% of those who died, against 44.0% and 75.4% for stage IV classification. Conclusions: COVID-era changes in stage and metastatic presentation were small in absolute terms, whereas the increase in treatment delay was larger and persisted. A diagnosis-time model modestly improved identification of early death beyond stage alone, flagging 28 more patients per 1000, of whom 21 died within the year. These findings support further evaluation; external validation is required before clinical use.