Search Results (756)

Smart cities are technologically advanced urban environments where interconnected systems and data-driven technologies enhance public service delivery and quality of life. These cities rely on information and communication technologies, the Internet of Things, big data, cloud computing, and other Industry 4.0 tools to support efficient city management and foster citizen engagement. Often referred to as digital cities, they integrate intelligent infrastructures and real-time data analytics to improve mobility, security, and sustainability. Ubiquitous sensors, paired with Artificial Intelligence, enable cities to monitor infrastructure, respond to residents’ needs, and optimize urban conditions dynamically. Given the increasing significance of Industry 4.0 in urban development, this study adopts a bibliometric approach to systematically review the application of these technologies within smart cities. Utilizing major academic databases such as Scopus and Web of Science the research aims to identify the primary Industry 4.0 technologies implemented in smart cities, assess their impact on infrastructure, economic systems, and urban communities, and explore the challenges and benefits associated with their integration. The bibliometric analysis included publications from 2016 to 2023, since the emergence of urban researchers’ interest in the technologies of the new industrial revolution. The task is to contribute to a deeper understanding of how smart cities evolve through the adoption of advanced technological frameworks. Research indicates that IoT and AI are the most commonly used tools in urban spaces, particularly in smart mobility and smart environments. Full article

The convergence of artificial intelligence (AI) and nanomedicine has transformed cancer vaccine development, particularly in optimizing RNA-loaded lipid nanoparticles (LNPs). Stability and targeted delivery are major obstacles to the clinical translation of promising RNA-LNP vaccines for cancer immunotherapy. This systematic review analyzes the AI’s impact on LNP engineering through machine learning-driven predictive models, generative adversarial networks (GANs) for novel lipid design, and neural network-enhanced biodistribution prediction. AI reduces the therapeutic development timeline through accelerated virtual screening of millions of lipid combinations, compared to conventional high-throughput screening. Furthermore, AI-optimized LNPs demonstrate improved tumor targeting. GAN-generated lipids show structural novelty while maintaining higher encapsulation efficiency; graph neural networks predict RNA-LNP binding affinity with high accuracy vs. experimental data; digital twins reduce lyophilization optimization from years to months; and federated learning models enable multi-institutional data sharing. We propose a framework to address key technical challenges: training data quality (min. 15,000 lipid structures), model interpretability (SHAP > 0.65), and regulatory compliance (21CFR Part 11). AI integration reduces manufacturing costs and makes personalized cancer vaccine affordable. Future directions need to prioritize quantum machine learning for stability prediction and edge computing for real-time formulation modifications. Full article

As global populations age, there is increasing demand for effective, person-centered healthcare solutions that support older adults to age in place. Home healthcare has emerged as a crucial strategy to address the complex health and social needs of older adults while reducing reliance on institutional care. This umbrella review aimed to synthesize evidence from existing systematic reviews and meta-analyses on home healthcare services and interventions targeting older adults. A comprehensive search was conducted across five databases and gray literature sources, including Google Scholar, for reviews published between 2000 and 2025. The review followed the Joanna Briggs Institute methodology and PRISMA statement. Twenty reviews met the inclusion criteria, encompassing a total of over 3.1 million participants. Interventions were grouped into four categories: integrated and multidisciplinary care, preventive and supportive home visits, technological and digital interventions, and physical, transitional, and environmental support. Results indicated that many interventions led to improved health outcomes, including enhanced functional ability, reduced hospital readmissions, and increased satisfaction. However, effectiveness varies depending on the intervention type, delivery model, and population. Challenges such as caregiver burden, digital exclusion, and implementation in diverse settings were noted. This review highlights the promise of home healthcare interventions and underscores the need for context-sensitive, equitable, and scalable models to support aging populations. Full article

Background: Social media platforms play a crucial role in contemporary healthcare, facilitating patient participation and enabling communication among healthcare workers, as well as serving as a platform for medical awareness and advocacy. Social media use among healthcare workers has increased to 91%, with 65% using it for health promotion purposes. Nonetheless, current studies have not properly and empirically explored its dimensions. Objectives: This study therefore examines social media dimensions and the productivity of healthcare workers. Methods: Leveraging the professional productivity theory and digital engagement theory, the study employs SPSS to analyze the gathered data through a partial least squares (PLS-SEM) approach to explore social media dimensions and productivity among healthcare workers in a Nigerian Tertiary Hospital. Based on a cross-sectional descriptive survey design and stratified random sampling method, 344 medical workers were analyzed. Findings: The study found that fear of missing out, information sharing, social influence, trust, and social media usage have a significant impact on the productivity of healthcare professionals. Conclusions: This research adds to the growing academic research on the capabilities of social media within the circular economic systems aimed at advancing healthcare delivery in developing economies. The research offers a method for maximizing the use of social media within healthcare settings to foster enhanced healthcare outcomes, particularly productivity. Full article

Background: Digital platforms and inclusive leadership are pivotal in modern healthcare, influencing organizational performance and patient outcomes. Despite the growing adoption of these factors, their combined impact on leadership effectiveness and patient care remains insufficiently understood. Prior research has primarily examined digital technology or leadership inclusivity separately, lacking integrative studies that address their joint effect on healthcare outcomes. There is a need to explore how these variables interact to improve leadership and patient-related metrics. Methods: This cross-sectional study surveyed 250 participants, including healthcare leaders, professionals, and patients, using structured questionnaires. The data analysis involved multiple regression, structural equation modeling (SEM), and hierarchical linear modeling (HLM) to examine the direct and hierarchical relationships among digital platform usage, leadership inclusivity, leadership effectiveness, and patient outcomes. Results: Leadership inclusivity showed a significant positive effect on leadership effectiveness (β = 0.16, p < 0.01) and patient satisfaction (β = 0.09, p < 0.05). Digital platform usage demonstrated a smaller but positive association with leadership effectiveness (β = 0.04) and patient satisfaction (β = 0.03). Leadership effectiveness was found to correlate moderately with patient safety (β = 0.23) and treatment efficacy (β = 0.25), with minimal organizational-level effects. Conclusions: This study uniquely integrates the adoption of digital technology with inclusive leadership, highlighting their synergistic influence on healthcare delivery. It advances the existing literature by providing quantitative evidence on how these elements interact to shape leadership and patient care outcomes. Full article

In the digital communication ecosystem, micro-influencers have influenced public response during crises, especially in complex geopolitical contexts. This paper introduces the micro-influencer impact model (MIIM), a framework for analyzing the impact of micro-influencers on crisis communication. The MIIM integrates four components (micro-influencer characteristics, message framing and delivery, audience factors, and crisis context) offering a comprehensive approach to understanding micro-influencer dynamics during crises. Cross-conflict analysis spanning Ukraine–Russia, Sudan–Ethiopia, Armenia–Azerbaijan, Myanmar, Syria, and India–Pakistan tensions demonstrates the MIIM’s broad applicability across diverse geopolitical crises, showing how factors like perceived authenticity, niche expertise, narrative personalization, and audience digital literacy consistently shape public opinion and crisis response. The MIIM synthesizes crisis communication theories, social influence models, and digital media research, providing a sophisticated framework for studying the dissemination of information and public engagement during crises. The paper proposes theoretically grounded propositions on the impact of micro-influencers, encompassing perceived authenticity, narrative framing, and influence over time, thereby laying the groundwork for future empirical research. Implications for communication scholars, crisis managers, policymakers, and social media platforms are discussed, emphasizing the MIIM’s relevance to theory and practice in crisis communication. Full article

This study investigates how topic-specific expression by women delivery riders on digital platforms predicts their community engagement, emphasizing the mediating role of self-disclosure and the moderating influence of cognitive and emotional language features. Using unsupervised topic modeling (Top2Vec, Topical Vectors via Embeddings and Clustering) and psycholinguistic analysis (LIWC, Linguistic Inquiry and Word Count), the paper extracted eleven thematic clusters and quantified self-disclosure intensity, cognitive complexity, and emotional polarity. A moderated mediation model was constructed to estimate the indirect and conditional effects of topic probability on engagement behaviors (likes, comments, and views) via self-disclosure. The results reveal that self-disclosure significantly mediates the influence of topical content on engagement, with emotional negativity amplifying and cognitive complexity selectively enhancing this pathway. Indirect effects differ across topics, highlighting the heterogeneous behavioral salience of expressive themes. The findings support a statistically grounded, semantically interpretable framework for predicting user behavior in high-dimensional text environments. This approach offers practical implications for optimizing algorithmic content ranking and fostering equitable visibility for marginalized digital labor groups. Full article

Accurately estimating the energy consumption of unmanned aerial vehicles (UAVs) in real-world delivery scenarios remains a critical challenge, particularly when UAVs operate in complex urban environments and are coupled with public transportation systems. Most existing models rely on oversimplified assumptions or static mission profiles, limiting their applicability to realistic, scalable drone-based logistics. In this paper, we propose a physically-grounded and scenario-aware energy sizing methodology for UAVs operating as part of a last-mile delivery system integrated with a city’s bus network. The model incorporates detailed physical dynamics—including lift, drag, thrust, and payload variations—and considers real-time mission constraints such as delivery execution windows and infrastructure interactions. To enhance the realism of the energy estimation, we integrate computational fluid dynamics (CFD) simulations that quantify the impact of surrounding structures and moving buses on UAV thrust efficiency. Four mission scenarios of increasing complexity are defined to evaluate the effects of delivery delays, obstacle-induced aerodynamic perturbations, and early return strategies on energy consumption. The methodology is applied to a real-world transport network in Belfort, France, using a graph-based digital twin. Results show that environmental and operational constraints can lead to up to 16% additional energy consumption compared to idealized mission models. The proposed framework provides a robust foundation for UAV battery sizing, mission planning, and sustainable integration of aerial delivery into multimodal urban transport systems. Full article

Digital transformation is reshaping the healthcare field by streamlining diagnostic workflows and improving disease management. Within this transformation, Digital Twins (DTs), which are virtual representations of physical systems continuously updated by real-world data, stand out for their ability to capture the complexity of human physiology and behavior. When coupled with Artificial Intelligence (AI), DTs enable data-driven experimentation, precise diagnostic support, and predictive modeling without posing direct risks to patients. However, their integration into healthcare requires careful consideration of ethical, regulatory, and safety constraints in light of the sensitivity and nonlinear nature of human data. In this review, we examine recent progress in DTs over the past seven years and explore broader trends in AI-augmented DTs, focusing particularly on movement rehabilitation. Our goal is to provide a comprehensive understanding of how DTs bolstered by AI can transform healthcare delivery, medical research, and personalized care. We discuss implementation challenges such as data privacy, clinical validation, and scalability along with opportunities for more efficient, safe, and patient-centered healthcare systems. By addressing these issues, this review highlights key insights and directions for future research to guide the proactive and ethical adoption of DTs in healthcare. Full article

This study explores the relationship between customer satisfaction and loyalty in mobile banking, emphasizing the moderating role of Technology Readiness. As mobile banking becomes increasingly central to financial service delivery, understanding the nuanced drivers of customer loyalty is essential for strategic growth. Drawing from the Technology Readiness Index, this study examines how four dimensions, optimism, innovativeness, discomfort, and insecurity, moderate the satisfaction–loyalty linkage. Data were collected via a structured survey from 258 mobile banking users in the United States, analyzed using partial least squares structural equation modeling (PLS-SEM). Results show that optimism and innovativeness positively moderate this relationship, while discomfort and insecurity act as negative moderators. Practically, this research introduces a segmented approach to mobile banking service design, underscoring the need for differentiated strategies that address varying levels of user readiness. Theoretically, this study addresses a gap in mobile banking literature by shifting the focus from adoption to sustained usage and satisfaction-based loyalty, enriching the discourse on customer behavior in digital finance. Full article

Under the impetus of digital rural development and the rapid advancement of smart logistics, intelligent terminal delivery technologies are gradually expanding into rural areas. This study employs a three-phase mixed research approach to systematically investigate the factors influencing and mechanisms underlying rural residents’ willingness to adopt smart logistics in Hebei Province. In the first phase, grounded theory is employed to identify seven key perceived factors: perceived usefulness, perceived ease of use, sensitivity to collective evaluation, cultural conservatism, infrastructure quality, facilitating conditions, and technological trust. In the second phase, integrating the TAM and the UTAUT, this study incorporates context-specific variables and conducts empirical analysis using SEM and the bootstrap method on 451 valid questionnaire responses. The results indicate that all factors except infrastructure quality significantly influence adoption willingness, with cultural conservatism exerting a negative effect. In the third phase, fsQCA is applied to identify eight configurations that lead to high adoption willingness, further supplementing and enriching the explanatory power of the SEM results. This research expands the theoretical understanding of smart logistics technology adoption mechanisms in rural areas and offers practical guidance for the promotion and application of related technologies. Full article

Background/Objectives: The introduction of composite attachments has greatly improved orthodontic aligner therapy, through better force delivery, more predictable movements, and enhanced retention. This in vitro study aims to present and investigate an innovative digital protocol for aligner attachment fabrication incorporating the latest 3D technology used in dentistry. Methods: A virtual attachment measuring 2.5 × 2 × 2 mm was designed using computer-aided design (CAD) software (Meshmixer, Autodesk Inc., San Francisco, CA, USA) and exported as an individual STL file. The attachments were fabricated using a digital light processing (DLP) 3D printer (model: Elegoo 4 DLP, Shenzhen, China) and a dental-grade biocompatible resin. A custom 3D-printed placement guide was used to ensure precise positioning of the attachments on the printed maxillary dental models. A flowable resin was applied to secure the attachments in place. Following attachment placement, the models were scanned using a laboratory desktop scanner (Optical 3D Smart Big, Open Technologies, Milano, Italy) and three intraoral scanners: iTero Element (Align Technology, Tempe, AZ, USA), Aoral 2, and Aoral 3 (Shining 3D, Hangzhou, China). Results: Upon comparison, the scans revealed that the iTero Element exhibited the highest precision, particularly in the attachment, with an RMSE of 0.022 mm and 95.04% of measurements falling within a ±100 µm tolerance. The Aoral 2 scanner showed greater variability, with the highest RMSE (0.041 mm) in the incisor area and wider deviation margins. Despite this, all scanners produced results within clinically acceptable limits. Conclusions: In the future, custom attachments made by 3D printing could be a valid alternative to the traditional composite attachments when it comes to improving aligner attachment production. While these preliminary findings support the potential applicability of such workflows, further in vivo research is necessary to confirm clinical usability. Full article

Background: Hypotension following epidural labor analgesia (ELA) is its most common complication, affecting approximately 20% of patients and posing risks to both maternal and fetal health. As digital tools and predictive analytics increasingly shape perioperative and obstetric anesthesia practices, real-world implementation data are needed to guide their integration into clinical care. Current monitoring practices rely on intermittent non-invasive blood pressure (NIBP) measurements, which may delay recognition and treatment of hypotension. The Hypotension Prediction Index (HPI) algorithm uses continuous arterial waveform monitoring to predict hypotension for potentially earlier intervention. This clinical trial evaluated the feasibility, acceptability, and efficacy of continuous HPI-guided treatment in reducing time-to-treatment for ELA-associated hypotension and improving maternal hemodynamics. Methods: This was a prospective randomized controlled trial design involving healthy pregnant individuals receiving ELA. Participants were randomized into two groups: Group CM (conventional monitoring with NIBP) and Group HPI (continuous noninvasive blood pressure monitoring). In Group HPI, hypotension treatment was guided by HPI output; in Group CM, treatment was based on NIBP readings. Feasibility, appropriateness, and acceptability outcomes were assessed among subjects and their bedside nurse using the Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM) instruments. The primary efficacy outcome was time-to-treatment of hypotension, defined as the duration between onset of hypotension and administration of a vasopressor or fluid therapy. This outcome was chosen to evaluate the clinical responsiveness enabled by HPI monitoring. Hypotension is defined as a mean arterial pressure (MAP) < 65 mmHg for more than 1 min in Group CM and an HPI threshold < 75 for more than 1 min in Group HPI. Secondary outcomes included total time in hypotension, vasopressor doses, and hemodynamic parameters. Results: There were 30 patients (Group HPI, n = 16; Group CM, n = 14) included in the final analysis. Subjects and clinicians alike rated the acceptability, appropriateness, and feasibility of the continuous monitoring device highly, with median scores ≥ 4 across all domains, indicating favorable perceptions of the intervention. The cumulative probability of time-to-treatment of hypotension was lower by 75 min after ELA initiation in Group HPI (65%) than Group CM (71%), although this difference was not statistically significant (log-rank p = 0.66). Mixed models indicated trends that Group HPI had higher cardiac output (β = 0.58, 95% confidence interval −0.18 to 1.34, p = 0.13) and lower systemic vascular resistance (β = −97.22, 95% confidence interval −200.84 to 6.40, p = 0.07) throughout the monitoring period. No differences were found in total vasopressor use or intravenous fluid administration. Conclusions: Continuous monitoring and precision hypotension treatment is feasible, appropriate, and acceptable to both patients and clinicians in a labor and delivery setting. These hypothesis-generating results support that HPI-guided treatment may be associated with hemodynamic trends that warrant further investigation to determine definitive efficacy in labor analgesia contexts. Full article

Background. Digital dentistry continues to evolve, offering improved accuracy, efficiency, and patient experience across various prosthodontic procedures. Many previous reviews have focused on digital applications in prosthodontics. But the use of a fully digital workflow for full-arch implant-supported prostheses in edentulous patients remains an emerging and underexplored area in the literature. Objective. This article presents a comprehensive methodological review of the digital workflow in full-arch implant-supported rehabilitation. It follows a structured literature exploration and synthesizes relevant technological processes from patient assessment to prosthetic delivery. Methods. The relevant literature was retrieved from the PubMed database on 20 June 2024, to identify the most recent and relevant studies. A total of 22 articles met the eligibility criteria and were included in the review. The majority included case and technical reports. Results. The review illustrates the integration and application of digital tools in implant dentistry, including cone-beam computed tomography (CBCT) exposure, intraoral scanning, digital smile design, virtual patients, guided surgery, and digital scanning. The key findings demonstrate multiple advantages of a fully digital workflow, such as reduced treatment time and cost, increased patient satisfaction, and improved interdisciplinary communication. Conclusions. Despite these benefits, limitations persist due to the low level of evidence, technological challenges, and the lack of standardized protocols. Further randomized controlled trials and long-term clinical evaluations are essential to validate the effectiveness and feasibility of a fully digital workflow for full-arch implant-supported rehabilitation. Full article

Background/Objectives: the precise application of torque during prosthetic screw tightening is essential to the long-term success and mechanical stability of implant-supported restorations. This study aimed to evaluate the influence of practitioner experience, glove material, screwdriver length, and hand moisture on the maximum torque value (MTV) generated during manual tightening. Methods: thirty participants, comprising 10 experienced professors and 20 senior dental students, performed tightening tasks under six hand conditions (nitrile gloves, latex gloves, and bare hands, each in dry and wet environments) using two screwdriver lengths (21 mm and 27 mm). The torque values were measured using a calibrated digital torque meter, and the results were analyzed using a linear mixed model. Results: professors applied significantly higher torque than students (16.92 Ncm vs. 15.03 Ncm; p = 0.008). Nitrile gloves yielded the highest torque (17.11 Ncm), surpassing bare hands significantly (p = 0.003). No statistically significant differences were found for screwdriver length (p = 0.12) or hand moisture (p = 0.11). Conclusions: these findings underscore the importance of clinical proficiency and glove material in torque delivery, providing evidence-based insights to enhance procedural reliability and training standards in implant prosthodontics. Full article