Rapid urbanisation and mounting climate risk have sharpened the focus on how urban green infrastructure sustains livability in increasingly data-driven cities. Although prior studies often isolate either spatial or digital dimensions, integrated evidence on their combined influence remains scarce. Addressing this gap, the present study investigated whether four complementary qualities of greenery, including spatial accessibility and connectivity, perceived physical quality, digital service integration and environmental value perception, jointly predict resident satisfaction. A questionnaire based on validated scales was distributed to residents of Ghent, Belgium, ensuring balanced representation across gender and age groups. Partial least-squares structural equation modelling with bootstrapping verified measurement robustness and estimated structural paths. All indicators exceeded accepted reliability and validity thresholds, and discriminant validity was confirmed through the Fornell–Larcker criterion and cross-loadings. The structural model explained the variance in satisfaction, revealing that walkable access to proximate, well-maintained green spaces produced the strongest positive effect, followed by moderate yet significant contributions from sensor-enabled services and recognition of ecosystem benefits. Our results underscore that walkable, high-quality green spaces provide the indispensable platform on which sensor-enabled services and compelling ecological narratives can truly thrive. To test the robustness and policy relevance of this framework, future studies should replicate it across a range of cultural and climatic settings. Ultimately, aligning place-based equity with digital innovation and environmental storytelling offers a scalable blueprint for building healthier, more resilient cities worldwide.
Distracted driving is becoming a growing problem in many countries, causing a huge number of road accidents and leading to death and serious injuries among drivers and pedestrians. Developing a distraction detection system has become a necessity to alert drivers and help reduce the number of road accidents. Thus, this paper presents a driver monitoring system designed to detect (1) the driver’s facial expression to identify whether they are drowsy or not, (2) facial front and sides monitoring, including hand movement, to detect (a) whether the driver is distracted or not, (b) a context-based distraction to specify the cause of such distraction. Our system utilizes lightweight image processing methods, including CNN-based models using EfficientNetB0. The model is efficient in achieving better accuracy with low computational resources, which makes it easier to put this system into real vehicles using mobile devices. We trained our model using a real-life dataset that contains numerous driver images in different situations to enhance its accuracy. The results show that the model achieved an accuracy of up to 95
This study presents a new sensor-integrated pen system that uses spiral drawing analysis to distinguish between essential tremor (ET) and Parkinson’s disease (PD). For the purpose of capturing motion and pressure data in real time, the system combines a tablet-based interface with a wireless electronic pen that is embedded with an accelerometer and gyroscope. Accurate tremor characterization is made possible by this dynamic handwriting analysis. A convolutional neural network (CNN) and an ensemble based on CatBoost were trained for classification; the ensemble performed better in PD detection and achieved 71.15
Urban resilience has evolved from a focus on physical infrastructure recovery to encompassing socio-economic, environmental, and governance dimensions. This paper presents an enhanced network-based framework for analyzing socio-economic resilience in cities, inspired by the Urban System Abstraction Hierarchy (USAH) model but extending it beyond acute shocks to address chronic stressors and multi-hazard contexts. We integrate insights from complex systems theory, socio-ecological resilience, and governance models to construct a comprehensive dependency matrix spanning seven domains, 40 indicators, and 451 sub-indicators. Using Vancouver as a case study, we mapped directed dependencies between indicators, enabling the identification of critical nodes through betweenness centrality. This approach highlights resilience bottlenecks where disruptions could cascade across multiple domains. We also conducted coupling analysis between domains to uncover interdependencies that may amplify risks. The results underscore the central role of Public Finance and Institutional Collaboration in sustaining city-wide resilience, suggesting targeted interventions for policy and investment priorities. The findings provide actionable insights for policymakers, urban planners, and stakeholders, offering a scalable methodology for other cities. This framework can guide resilience retrofitting, cross-sector collaboration, and informed decision-making, ultimately fostering adaptive, inclusive, and sustainable urban systems.
The rising concern of career-related anxiety and depression among university students poses a serious mental health challenge. To address this, we propose an explainable AI framework designed to assist early detection of such mental health conditions. Our approach integrates two primary types of data: publicly available survey data from Kaggle and gesture-based data collected through video interviews of university students. These datasets capture diverse behavioral and emotional cues relevant to mental health assessments. We applied robust preprocessing techniques tailored to each data modality. For structured survey responses, standard normalization and missing value handling were employed, while facial expression data such as head pose, gaze direction, and muscle movement intensities were processed using facial action coding and emotion feature extraction. An intermediate fusion neural network with attention mechanisms was utilized to effectively combine the two data streams. To improve model robustness, label smoothing was incorporated during training. To preserve user privacy and enable collaboration across institutions, federated learning (FL) was used, allowing model training without sharing raw data. For interpretability, we adopted Integrated Gradients and SHAP to identify important features influencing predictions. On the Kaggle survey data, our model achieved an accuracy of 91.76
In this study, a two-layer artificial neural network (ANN) is used to predict the aerodynamic lift and drag coefficients for NACA0005. The first layer has nine neurons, while the number of neurons in the second layer is varied from 1 to 100 to find the best performance. Data from 28 angles of attack ranges 9° ≤ α ≤ 11°, and the Reynolds number between 1000 and 5000 are utilized. The ANN achieved R2 > 0.99 for lift coefficient (Cl) and R2 > 0.96 for drag coefficient (Cd), showing high prediction accuracy. Reynolds number of 1750 and 3000 are evaluated as validation and testing to assess the accuracy of ANN. The overall ANN predicts the computational fluid dynamics results, with errors below 1
Ethical business practices are shaped by a complex interplay of legal mandates, corporate policies, and organizational culture. This paper explores the cascading structure of legal compliance, from international laws to local regulations, and their impact on business operations. It examines key federal laws such as HIPAA and FERPA, highlighting their role in protecting sensitive information. Additionally, the study delves into corporate policies—including employee conduct, time off, NDAs, and non-compete clauses—emphasizing their role in shaping workplace ethics. Beyond compliance, the research underscores the significance of ethical leadership in fostering a positive organizational culture that balances legal obligations with corporate responsibility. By integrating legal, ethical, and cultural perspectives, businesses can create a sustainable and ethical work environment that benefits both employees and stakeholders.
Extended Reality (XR) is rapidly reshaping healthcare education in South Korea, with nursing emerging as a key area of application. This paper critically examines Korea’s integration of XR into nursing pedagogy, exploring conceptual frameworks, implementation strategies, and policy drivers that distinguish the Korean approach from global trends. Grounded in experiential learning theory and mastery-based training, XR enables iterative skill development through immersive clinical simulations that enhance knowledge retention, procedural accuracy, and learner motivation. Korea’s robust 5G infrastructure, centralized healthcare education system, and coordinated public–private initiatives have facilitated the scaling of XR beyond pilot projects into systemic curricular reforms. Applications span core nursing skills, pediatric and trauma care, emergency triage, and mental health training, with growing use of metaverse platforms, AI, and wearable technologies. The study identifies persistent challenges, high equipment costs, limited standardized content, faculty training gaps, and ergonomic issues with head-mounted displays, yet highlights how Korea mitigates these through government investment, policy alignment, and collaborative innovation ecosystems. By situating Korean developments within global XR education trends, the paper underscores Korea’s role in setting international benchmarks for immersive healthcare training. The findings provide actionable insights for scaling XR adoption, emphasizing the need for standardized evaluation tools, ethical safeguards, and integration with emerging digital health infrastructures. Ultimately, Korea’s XR-driven transformation offers a model for other nations seeking to modernize nursing education, bridge theoretical and clinical learning, and prepare future professionals for increasingly complex healthcare environments.
In recent years, the analysis of histopathology images has become increasingly important for supporting accurate and timely cancer diagnosis. However, developing a unified framework capable of handling multiple critical tasks, such as nucleus segmentation, classification, and count regression, remains a significant challenge. In this study, we propose MedViT-HoVer++(ViT), a transformer-guided multitask learning framework that integrates convolutional backbones with Vision Transformers to enhance both global context and fine-grained feature representation. Our method leverages a count-aware loss function to address challenges in nuclei overlap and density variation and incorporates fixed-shape handling to improve generalizability across datasets. We evaluate our model on the CoNIC challenge dataset and benchmark it against established architectures such as ResNet, demonstrating substantial improvements in Dice scores for segmentation, classification accuracy, and counting precision. The experimental results confirm that MedViT-HoVer++(ViT) consistently outperforms traditional CNN-based models across all three tasks. These findings highlight the potential of our unified framework to support more reliable, efficient, and scalable computational pathology tools, which may aid pathologists in routine diagnostic workflows and research applications.
Hyperparameter tuning involves selecting the most suitable parameters that can enhance the overall performance of reinforcement learning algorithms like Q-learning. Traditional approaches, including manual and random tuning, are often inefficient and inconsistent. This paper introduces an automated hyperparameter tuning approach that utilizes FOX optimization algorithm with Q-learning (Q-FOX). Furthermore, a novel fitness function is introduced to prioritize reward while also considering temporal difference error and convergence time. Q-FOX starts by running the FOX and tries to minimize the fitness value derived from observations at each iteration by executing the Q-learning. The proposed approach has been tested on two control tasks from the OpenAI Gym: Cart Pole and Frozen Lake. Experimental results show that Q-FOX outperforms other optimization techniques that integrated with Q-learning, including particle swarm optimization, bee’s algorithm, genetic algorithm, and the random search methods. However, Q-FOX increased rewards by 36
Density Functional Theory (DFT) investigations of functionalized fullerenes (C60, C60(OH), and C60(COOH)) reveal crucial structure-activity relationships governing their interactions with reactive oxygen species. The study demonstrates how chemical modifications transform pristine C60 from a relatively inert structure (binding energy − 5.2 to − 8.4 kcal/mol, HOMO-LUMO gap 3.1–3.5 eV, charge transfer 0.12–0.15e−) into potent radical scavengers through systematic enhancement of binding energetics and electronic properties. Functional group incorporation shows an improvement of antioxidant performance, with binding strength progressing to − 12.3 to − 15.1 kcal/mol for hydroxylated derivatives (C60(OH)) through hydrogen bonding and reaching − 14.7 to − 18.9 kcal/mol for carboxylated systems (C60(COOH)) via proton-coupled electron transfer. This change directly correlates with decreasing HOMO-LUMO gaps (2.6–2.9 eV for C60(OH) and 2.3–2.7 eV for C60(COOH)) and increasing charge-transfer capacity (0.18–0.22e− for C60(OH) and 0.25–0.30e− for C60(COOH)). The presented analysis shows carboxylated fullerenes as particularly effective scavengers due to their dual capacity for electron delocalization and proton exchange mechanisms. Hydroxylated derivatives are also promising alternatives, offering balanced performance with potentially improved biocompatibility. Spatial organization of functional groups is important, with distributed configurations demonstrating greater stability than clustered arrangements. These theoretical predictions, validated through advanced computational models (ωB97X-D/def2-TZVP with PCM solvation, ε = 78.4) incorporating dispersion corrections (D3) and solvation effects, establish fundamental design principles for carbon-based antioxidants. The comprehensive methodology combines structural optimization (convergence criteria: energy 1 × 10−6 Hartree, force 4.5 × 10−4 Hartree/Bohr) with electronic structure analysis to provide reliable structure-property relationships, which are invaluable in developing tailored antioxidant materials with applications in biomedicine and materials science. The rigorous computational approach ensures robust predictions while accommodating adaptation to related systems and reactive species.
In the dynamic landscape of modern urban environments, ensuring public safety in smart cities has become increasingly critical. This study proposes an advanced surveillance system designed to detect robbery using cutting edge machine learning (ML) and self-supervised learning methods. By leveraging the UCF-Crime dataset, the system employs SimSiam-based architecture with a ResNet-18 backbone to extract robust visual features without requiring extensive labeled data. Through data augmentation and efficient preprocessing, the model achieves 97–99
The healthcare sector is undergoing a profound digital transformation, with cloud-based compliance architectures emerging as essential frameworks for navigating complex regulatory requirements while enhancing operational efficiency. This article examines how healthcare organizations grapple with exponential data growth and increasingly stringent compliance demands that traditional legacy systems struggle to address. Cloud solutions offer transformative capabilities for healthcare compliance, enabling secure data environments that adapt to evolving regulations while significantly reducing documentation errors and integration challenges. However, these implementations present critical challenges, including data sovereignty restrictions, vendor dependency risks, algorithmic bias in automated systems, and patient trust concerns. The evolving landscape encompasses multiple regulatory frameworks that modern cloud architectures can systematically address through advanced implementation strategies, though organizations must carefully balance benefits against emerging ethical and operational risks. Hybrid and multi-cloud approaches allow organizations to maintain strict control over sensitive information while leveraging public cloud capabilities, with edge computing providing complementary solutions for real-time compliance monitoring. Together, these technologies are fundamentally changing how healthcare organizations approach regulatory demands, shifting compliance from an administrative burden to a strategic advantage while requiring careful attention to ethical considerations and stakeholder trust.
Strategic management has evolved as a critical discipline shaping business success and organizational competitiveness. This study conducts a retrospective bibliometric and knowledge diffusion analysis of leading strategic management research from 1985 to 2023 using data harvested from Scopus. The analysis comprises two parts: first, a bibliometric evaluation utilizing science mapping techniques to analyze citation networks, co-citation patterns, co-author collaborations, and country-level research contributions in strategic management. Second, a knowledge diffusion analysis investigates how strategic management research has influenced other disciplines through citation networks and interdisciplinary research expansion. By mapping the academic landscape, identifying key contributors, and assessing the global impact of strategic management research, this study provides valuable insights into the evolution and knowledge dissemination of the field. The findings contribute to a deeper understanding of how strategic management theories and frameworks extend beyond their primary domain, influencing business, economics, technology, and public policy.
Background: Lower back pain is a major occupational risk for caregivers. Wearable Robotic Lumbar Support (WRLS) devices aim to reduce this burden, and their effectiveness is typically assessed via surface electromyography (sEMG) using A/B test designs. However, A/B tests are not well-suited to real caregiving environments, where task complexity and individual differences introduce uncontrollable variability. Methods: We propose a Bayesian Network-based evaluation method as an alternative to conventional A/B tests. This approach estimates assistive effects by statistically separating them from task- and motion-related variability. Results: In a field study with two professional caregivers using WRLS, conventional analysis showed a 23.6
This study analyzes the rapid emergence of Digital Therapeutics (DTx) in South Korea and positions the country’s regulatory and translational framework as a potential model for global digital health innovation. Unlike conventional pharmacological treatments, DTx are software-based, clinically validated interventions delivered through mobile platforms, wearables, and immersive technologies. While DTx have gained global attention, comprehensive analyses of Korea’s distinctive approach, characterized by centralized insurance, adaptive reimbursement models, and AI-enabled biosensing, remain limited. Addressing this gap, the paper synthesizes regulatory documents, peer-reviewed studies, and real-world case analyses, including Korea’s first-approved insomnia DTx (Somzz and WELT-I), to map a four-phase translational pathway from design to post-market monitoring. Findings highlight how Korea’s integrated infrastructure and pilot-oriented reimbursement schemes accelerate adoption across conditions such as insomnia, panic disorder, and chronic respiratory diseases. The paper’s novelty lies in contrasting Korea’s unified approach with more fragmented frameworks in other regions, offering a scalable model for harmonized regulation and evidence generation in software-driven therapeutics.
The closure of blood flow to brain tissues causes medical stroke which releases damage to brain cells. The detection of strokes in brain CT images remains essential to execute prompt medical care. Computerized Tomography image patterns show potential identification through machine learning techniques along with deep learning approaches. For automating this procedure, the research employs multiple ML and DL methods including the artificial intelligence system features Convolutional Neural Network (CNN) together with Transfer Learning using VGG16 and Resnet50 as well as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) and Decision Tree and Random Forest and XGBoost algorithms. This study aims to develop an effective way to detect brain stroke as a significant medical issue. Multiple experiments with ML and DL approaches took place through our research. An experiment was conducted with 1612 CT images used for training and 403 CT images employed for evaluation. Our custom-designed CNN model utilized multi-convolution layers. Performance analysis of the algorithms took place based on the analytical performance of the models evaluated for brain stroke diagnostics was evaluated based on accuracy, recall, f1, precision and False Negative Rate (FNR). The customized CNN model achieved the highest accuracy level of 97.00
RAG (Retrieval Augmented Generation) is the process of integrating the output generated from LLM (Large Language Model) and embedding it with external knowledge, without the need to retrain the model. Many studies have been conducted to evaluate the integration of RAG with LLMs. However, they have rarely compared it with cloud-based chatbots that address the industry's requirements, which this research aims to address. A systematic literature review was conducted, followed by interviews with industry participants to gather requirements for chatbots. Based on the interview analysis, the RAG-LLM chatbot was implemented, and a comparative study was conducted between RAG-LLM and cloud-based (Microsoft Azure) chatbots. The experimental evaluation showed that RAG-chatbot achieved high performance, as indicated by expert evaluation (96
Respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) are influenced by both genetic and environmental factors. This research explores the potential of integrating genomic and environmental data using machine learning algorithms to build robust predictive models for respiratory health. A synthetic dataset was created to simulate real-world conditions, including single-nucleotide polymorphisms (SNPs), PM2.5 levels, smoke exposure, lung function metrics, and asthma severity classifications. Data preprocessing steps included normalization, imputation of missing values, and TF-IDF vectorization for text-based features. Several machine learning algorithms were used, such as Natural Language Processing (NLP), Decision Tree, Random Forest, and Convolutional Neural Networks (CNNs). The Synthetic Minority Over-sampling Technique (SMOTE) was used to address class imbalance. Random Forest demonstrated significant results, achieving 98
Background: Wheelchair users often experience major health-related incidents, resulting in an increased number of injuries and hospitalizations, thereby reducing independence. Current assistive solutions are generally proprietary, expensive, not retrofittable, or limited to monitoring a single function. Methods: In this paper, we introduce a low-cost, AI-driven, Internet-of-Things (IoT)-integrated monitoring and alerting system specifically designed as a universal retrofittable solution for wheelchair users who require close monitoring. We implemented this system using a Raspberry Pi 5, a smart wearable device (Samsung Galaxy Watch 5 smartwatch), Inertial Measurement Unit (IMU) sensors, and a camera module for local edge AI facial detection. It provides users with fall detection, vital sign monitoring, and anomaly alerts in real-time. Our system performance was quantitatively evaluated using industry-standard techniques for end-to-end latency, load testing, computational resource efficiency, and code quality via SonarQube (ISO) static analysis. Results: The AI-driven prototype demonstrated rapid end-to-end alert latency (mean < 150 ms), robust operation under load conditions (<5
