Search Results (6,279)

Background: Auricular defects resulting from congenital anomalies, trauma, or oncologic resection pose significant functional and psychosocial challenges. When autologous reconstruction is not feasible or not desired, implant-retained auricular prostheses represent a reliable alternative with high patient satisfaction. This study aimed to systematically evaluate the clinical performance of craniofacial implants used for auricular prosthetic rehabilitation, focusing on implant survival, prosthetic outcomes, workflow typologies, and complications. A secondary objective was to illustrate the long-term validity of a minimally invasive navigation technique through a clinical case with 6-year follow-up. Methods: A systematic review was conducted according to PRISMA guidelines. Clinical studies published between 2005 and 2025 reporting outcomes of implant-retained auricular prostheses were searched in PubMed and Scopus databases. Data were extracted on implant type, survival rates, prosthetic performance, workflow, and complications. Risk of bias was assessed using appropriate tools based on each study design. Results: A total of thirty-two studies were included, comprising fifteen case reports, fifteen case series, one cohort study, and one prospective observational study. Implant survival was consistently high across all workflow categories, with failures predominantly associated with irradiated or anatomically compromised bone. Prosthetic outcomes were favorable, showing excellent esthetics, stable retention, and high patient satisfaction irrespective of manufacturing method, although digital and navigation-assisted workflows improved reproducibility, symmetry, and planning precision. Complication rates were low and generally limited to mild peri-abutment inflammation manageable with conservative care. The clinical case confirmed these findings, showing stable osseointegration, healthy soft tissues, and uncompromised prosthetic function at 6-year follow-up. Conclusions: Implant-retained auricular prostheses show predictable long-term success, independent of whether traditional, hybrid, or fully digital workflows are employed. Digital technologies enhance surgical accuracy, minimize morbidity, and streamline prosthetic fabrication, although high-quality comparative studies remain limited. Full article

The present study explores the relationship between the systemic approach, educational innovation, and the use of digital technologies in higher education, with an emphasis on military academies. The aim of the research is to shed light on how systemic thinking can support strategic planning, the quality of education, and the effective integration of innovative practices, such as artificial intelligence, information and communication technologies, and virtual reality. The methodology was based on quantitative research using a questionnaire, which was distributed to 452 members of the Hellenic Non-Commissioned Officers Academy educational community (teaching staff, cadets, and recent graduates). Data analysis showed that the adoption of a systemic approach is positively associated with the readiness of trainers, including both instructors and future professionals (cadets), to support and implement educational innovations. Furthermore, it was found that the clarity of educational objectives and the alignment of critical elements of the educational system (resources, technology, instructors, trainees, and processes) significantly reinforce the intention to adopt innovative practices. The findings also show that educators’ positive perceptions of artificial intelligence and virtual/augmented reality are associated with a higher appreciation of learning benefits, such as improved performance, trainee satisfaction, and collaboration. In contrast, demographic and professional factors have a limited effect on attitudes toward innovation. Overall, findings indicated that innovation in military academies is not limited to the technological dimension, but requires a holistic, systemic approach that integrates organizational, pedagogical, and strategic parameters. The study contributes both theoretically and practically, providing empirical evidence for the role of systemic thinking in the design and implementation of innovative educational policies in military and broader academic education. Full article

Globally, grasslands, savannas, and wetlands are degrading rapidly and increasingly being replaced by woody vegetation. Woody Plant Encroachment (WPE) disrupts natural landscapes and has significant consequences for biodiversity, ecosystem functioning, and key ecosystem services. This review synthesizes findings from 159 peer-reviewed studies identified through a PRISMA-guided systematic literature review to evaluate the drivers of WPE, its ecological impacts, and the remote sensing (RS) approaches used to monitor it. The drivers of WPE are multifaceted, involving interactions among climate variability, topographic and edaphic conditions, hydrological change, land use transitions, and altered fire and grazing regimes, while its impacts are similarly diverse, influencing land cover structure, water and nutrient cycles, carbon and nitrogen dynamics, and broader implications for ecosystem resilience. Over the past two decades, RS has become central to WPE monitoring, with studies employing classification techniques, spectral mixture analysis, object-based image analysis, change detection, thresholding, landscape pattern and fragmentation metrics, and increasingly, machine learning and deep learning methods. Looking forward, emerging advances such as multi-sensor fusion (optical– synthetic aperture radar (SAR), Light Detection and Ranging (LiDAR)–hyperspectral), cloud-based platforms including Google Earth Engine, Microsoft Planetary Computer, and Digital Earth, and geospatial foundation models offer new opportunities for scalable, automated, and long-term monitoring. Despite these innovations, challenges remain in detecting early-stage encroachment, subcanopy woody growth, and species-specific patterns across heterogeneous landscapes. Key knowledge gaps highlighted in this review include the need for long-term monitoring frameworks, improved socio-ecological integration, species- and ecosystem-specific RS approaches, better utilization of SAR, and broader adoption of analysis-ready data and open-source platforms. Addressing these gaps will enable more effective, context-specific strategies to monitor, manage, and mitigate WPE in rapidly changing environments. Full article

Background/Objectives: The growing number of older adults with complex healthcare needs increases demand for homecare services, while a shrinking workforce often lacks skills for advanced tasks. Digital health is seen as a promising tool to address these challenges. This study explored Swedish homecare professionals’ perceptions of the potential use of digital twins in daily work. Methods: Four focus group discussions were conducted with 31 homecare professionals; two groups each in one urban/rural and one rural municipality. Data were analyzed using inductive content analysis. Results: Three main themes emerged: (i) Perceptions of digital twins as support for older adults, including needs-based, individualized care and proactive support enabling preventive measures; (ii) Perceptions of digital twins as support for professionals, including a better work environment through streamlined tasks and flows and enhanced planning and assessment; and (iii) Concerns about digital twins, focusing on ethical and social issues and limited understanding, which were related to monitoring aspects, the importance of physical visits, and how the technology works. Conclusions: Digital twins are perceived by professionals to have the potential to improve homecare services by supporting both older adults and professionals; however, further research is needed to address concerns and practical implications. Full article

Background: Artificial intelligence (AI) in urban and multimodal transport has demonstrated strong potential; however, real-world deployment remains constrained by limited governance-ready design, fragmented data ecosystems, and single-objective optimization practices. The resulting problem is that agencies lack a reproducible, deployment-ready architecture that links data fusion, multi-objective optimization, and electrification constraints into daily multimodal operational decision making. Methods: This study presents a systematic review and synthesis of 145 peer-reviewed studies on network control, green routing, digital twins, and electric-bus scheduling, conducted in accordance with PRISMA 2020 using predefined inclusion and exclusion criteria. Based on these findings, a deployment-oriented operational AI framework is developed. Results: The proposed architecture comprises five interoperable layers—data ingestion, streaming analytics, optimization services, decision evaluation, and governance monitoring—supporting scalability, reproducibility, and transparency. Rather than producing a single optimal solution, the framework provides decision-ready trade-offs across service quality, cost efficiency, and sustainability while accounting for uncertainty, reliability, and electrification constraints. The approach is solver-agnostic, supporting evolutionary and learning-based techniques. Conclusions: A Thessaloniki-based multimodal case study demonstrates how reproducible AI workflows can connect real-time data streams, optimization, and institutional decision making for continuous multimodal transport management under operational constraints. Full article

In the context of the “Dual Carbon” goals, achieving synergistic development between digitalization and green transformation in the coal power industry is essential for ensuring a just and sustainable energy transition. The core scientific problem addressed is the lack of a robust quantitative tool to evaluate the comprehensive performance of diverse transition scenarios in a complex environment characterized by multi-objective trade-offs and high uncertainty. This study establishes a sustainability-oriented four-dimensional performance evaluation system encompassing 22 indicators, covering Synergistic Economic Performance, Green-Digital Strategy, Synergistic Governance, and Technology Performance. Based on this framework, a Fuzzy DEMATEL–MultiMOORA–Borda integrated decision model is proposed to evaluate seven transition scenarios. The computational framework utilizes the Interval Type-2 Fuzzy DEMATEL (IT2FS-DEMATEL) method for robust causal analysis and weight determination, addressing the inherent subjectivity and vagueness in expert judgments. The model integrates MultiMOORA with Borda Count aggregation for enhanced ranking stability. All model calculations were implemented using Matlab R2022a. Results reveal that Carbon Price and Digital Hedging Capability (C13) and Digital-Driven Operational Efficiency (C43) are the primary drivers of synergistic performance. Among the scenarios, P3 (Digital Twin Empowerment and New Energy Co-integration) achieves the best overall performance (score: 0.5641), representing the most viable pathway for balancing industrial efficiency and environmental stewardship. Robustness tests demonstrate that the proposed model significantly outperforms conventional approaches such as Fuzzy AHP (Analytic Hierarchy Process) and TOPSIS under weight perturbations. Sensitivity analysis further identifies Financial Return (C44) and Green Transformation Marginal Economy (C11) as critical factors for long-term policy effectiveness. This study provides a data-driven framework and a robust decision-support tool for advancing the coal power industry’s low-carbon, intelligent, and resilient transition in alignment with global sustainability targets. Full article

Engineering is suffering a significant change driven by the integration of artificial intelligence (AI) into engineering optimization in design, analysis, and operational efficiency across numerous disciplines. This review synthesizes the current landscape of AI-driven optimization methodologies and their impacts on engineering applications. In the literature, several frameworks for AI-based engineering optimization have been identified: (1) machine learning models are trained as objective and constraint functions for optimization problems; (2) machine learning techniques are used to improve the efficiency of optimization algorithms; (3) neural networks approximate complex simulation models such as finite element analysis (FEA) and computational fluid dynamics (CFD) and this makes it possible to optimize complex engineering systems; and (4) machine learning predicts design parameters/initial solutions that are subsequently optimized. Fundamental AI technologies, such as artificial neural networks and deep learning, are examined in this paper, along with commonly used AI-assisted optimization strategies. Representative applications of AI-driven engineering optimization have been surveyed in this paper across multiple fields, including mechanical and aerospace engineering, civil engineering, electrical and computer engineering, chemical and materials engineering, energy and management. These studies demonstrate how AI enables significant improvements in computational modelling, predictive analytics, and generative design while effectively handling complex multi-objective constraints. Despite these advancements, challenges remain in areas such as data quality, model interpretability, and computational cost, particularly in real-time environments. Through a systematic analysis of recent case studies and emerging trends, this paper provides a critical assessment of the state of the art and identifies promising research directions, including physics-informed neural networks, digital twins, and human–AI collaborative optimization frameworks. The findings highlight AI’s potential to redefine engineering optimization paradigms, while emphasizing the need for robust, scalable, and ethically aligned implementations. Full article

Background/Objectives: This study examines the application of the novel double emulsion gel system for the delivery and release of encapsulated cannabidiol (CBD) and the probiotic strain Lactiplantibacillus plantarum DSM 24624. Methods: During a six-week experimental period comprising stabilization, treatment, and wash-out phases, the dynamic Simulator of the Human Intestinal Microbial Ecosystem (SHIME^®) model was employed to assess a system. The evaluation focused on the delivery of CBD and probiotics, as well as the system’s effects on microbial composition, diversity, and metabolic activity throughout the digestion process using 16S rRNA gene sequencing and digital PCR methods. Results: Microbial community analysis revealed significant shifts in both mucosal and luminal microbiota following supplementation. The treatment increased beneficial bacterial families such as Lachnospiraceae and Clostridiaceae, demonstrated effective delivery, release, and persistence of the probiotic L. plantarum, as well as enhanced butyrate and lactate production. Diversity analyses highlighted a transient rise in alpha diversity within the mucin layer and a decrease in the lumen, with significant changes in beta diversity across experimental phases. Conclusions: Findings suggest that double emulsion gel can be employed for the delivery of probiotics and CBD to the gastrointestinal tract. In addition, an innovative CBD-probiotic formulation can modulate gut microbiota composition and metabolic activity, suggesting its potential as a functional food innovation for intestinal health. However, the results are based on an in vitro model, which lacks the complexity of the human host environment, and further clinical studies are necessary to confirm the biological relevance and therapeutic potential of such delivery systems for gastrointestinal health. Full article

Background: Cold chain distribution in Agri-Biotech supply chains faces serious challenges due to strict time windows, high temperature sensitivity, and conflict between different operational objectives, and conventional static approaches are unable to address these complexities. Methods: In this study, an integrated decision support framework is presented that combines multi-objective fuzzy modeling and an adaptive digital twin to simultaneously manage logistics costs, product quality degradation, and service time compliance under operational uncertainty. Key uncertain parameters are modeled using triangular fuzzy numbers, and the digital twin dynamically updates the decision parameters based on operational information. The proposed framework is evaluated using real industrial data and comprehensive computational experiments. Results: The results show that the proposed approach is able to produce stable and balanced solutions, provides near-optimal performance in benchmark cases, and is highly robust to demand fluctuations and temperature deviations. Digital twin activation significantly improves the convergence behavior and stability of the solutions. Conclusions: The proposed framework provides a reliable and practical tool for adaptive planning of cold chain distribution in Agri-Biotech industries and effectively reduces the gap between advanced optimization models and real-world operational requirements. Full article

Background and Objectives: Academic point-of-care (POC) manufacturing enables the in-hospital design and production of patient-specific medical devices within certified environments, integrating clinical practice, engineering, and translational research. This model represents a new academic ecosystem that accelerates innovation while maintaining compliance with medical device regulations. Gregorio Marañón University Hospital has established one of the first ISO 13485-certified academic manufacturing facilities in Spain, providing on-site production of anatomical models, surgical guides, and custom implants for oral and maxillofacial surgery. This study presents a retrospective review of all devices produced between April 2017 and September 2025, analyzing their typology, materials, production parameters, and clinical applications. Materials and Methods: A descriptive, retrospective study was conducted on 442 3D-printed medical devices fabricated for oral and maxillofacial surgical cases. Recorded variables included device classification, indication, printing technology, material type, sterilization method, working and printing times, and clinical utility. Image segmentation and design were performed using 3D Slicer and Meshmixer. Manufacturing used fused deposition modeling (FDM) and stereolithography (SLA) technologies with PLA and biocompatible resin (Biomed Clear V1). Data were analyzed descriptively. Results: During the eight-year period, 442 devices were manufactured. Biomodels constituted the majority (approximately 68%), followed by surgical guides (20%) and patient-specific implants (7%). Trauma and oncology were the leading clinical indications, representing 45% and 33% of all devices, respectively. The orbital region was the most frequent anatomical site. FDM accounted for 63% of the printing technologies used, and PLA was the predominant material. The mean working time per device was 3.4 h and mean printing time 12.6 h. Most devices were applied to preoperative planning (59%) or intraoperative use (35%). Conclusions: Academic POC manufacturing offers a sustainable, clinically integrated model for translating digital workflows and additive manufacturing into daily surgical practice. The eight-year experience of Gregorio Marañón University Hospital demonstrates how academic production units can enhance surgical precision, accelerate innovation, and ensure regulatory compliance while promoting education and translational research in healthcare. Full article

This study provides a quantitative assessment of the combined effects of progressive urbanization and changes in precipitation patterns (PPs) on the urban water cycle. The primary objective was to evaluate historical (1940–2024) and projected (to 2060) changes in total annual surface runoff (TSR) and retention capacity (RC) in the highly urbanized catchment of the Dłubnia River in Cracow, Poland. Simulations were performed using the EPA SWMM hydrodynamic model, supported by digitized historical land-use maps and long-term meteorological records. The results demonstrate that the dominant driver of the observed 6.4-fold increase in TSR and 6.8-fold loss of retention capacity (LRC) over the study period was the progressive increase in impervious surfaces. Although inter-annual variability in the amount and structure of annual precipitation (AP) strongly correlates with annual TSR (r = 0.97), its contribution to the long-term upward trend in TSR is marginal (r = 0.19). Land use and land cover change (LULC) exhibits an extremely strong correlation with the long-term TSR trend (r = 0.998). The study also highlights the high effectiveness of nature-based solutions (NbSs), particularly bioretention cells (BCs)/rain gardens, in mitigating the adverse hydrological effects of excessive surface sealing. Implementation of BCs covering just 3–4% of the total drained roof and road area is sufficient to fully offset the projected combined negative impacts of further urbanization and climate change (CC) in scope Representative Concentration Pathways (RCP4.5 and RCP8.5) projections on catchment retention capacity by 2060. These findings position strategically targeted, relatively small-scale bioretention as one of the most effective and feasible urban adaptation measures in mature, densely developed cities. Full article

Balancing convergence accuracy and population diversity remains a fundamental challenge in multi-objective optimization, particularly for complex and constrained engineering problems. To address this issue, this paper proposes a novel Multi-Objective Crested Porcupine Optimizer (MOCPO), inspired by the hierarchical defensive behaviors of crested porcupines. The proposed algorithm integrates four biologically motivated defense strategies—vision, hearing, scent diffusion, and physical attack—into a unified optimization framework, where global exploration and local exploitation are dynamically coordinated. To effectively extend the original optimizer to multi-objective scenarios, MOCPO incorporates a reference-point guided external archiving mechanism to preserve a well-distributed set of non-dominated solutions, along with an environmental selection strategy that adaptively partitions the objective space and enhances solution quality. Furthermore, a multi-level leadership mechanism based on Euclidean distance is introduced to provide region-specific guidance, enabling precise and uniform coverage of the Pareto front. The performance of MOCPO is comprehensively evaluated on 18 benchmark problems from the WFG and CF test suites. Experimental results demonstrate that MOCPO consistently outperforms several state-of-the-art multi-objective algorithms, including MOPSO and NSGA-III, in terms of IGD, GD, HV, and Spread metrics, achieving the best overall ranking in Friedman statistical tests. Notably, the proposed algorithm exhibits strong robustness on discontinuous, multimodal, and constrained Pareto fronts. In addition, MOCPO is applied to UAV path planning in four complex terrain scenarios constructed from real digital elevation data. The results show that MOCPO generates shorter, smoother, and more stable flight paths while effectively balancing route length, threat avoidance, flight altitude, and trajectory smoothness. These findings confirm the effectiveness, robustness, and practical applicability of MOCPO for solving complex real-world multi-objective optimization problems. Full article

Background/Objectives: Although numerous studies have examined associations between screen time and early language development, less is known about how screen exposure interacts with developmental stage, digital content quality, and parental mediation across childhood and adolescence, particularly with respect to pragmatic communication. This study aimed to address these gaps by examining the joint associations of screen time, content composition, and parental mediation with multiple linguistic and pragmatic domains across a broad age range. Methods: A cross-sectional study was conducted with 286 Romanian participants aged 5–19 years, grouped into four developmental stages. Measures included daily screen time, proportion of educational versus recreational content, parental mediation practices, and standardized assessments of vocabulary, verbal fluency, grammatical competence, and pragmatic communication. Analyses included descriptive statistics, Pearson correlations, 4 × 3 factorial ANOVAs (age group × screen-time category), and multiple linear regression. Results: Higher levels of screen exposure were consistently associated with lower performance across all linguistic and pragmatic domains (r = −0.19 to −0.28, all p < 0.01). Participants viewing >2 h/day showed significantly weaker outcomes than those with ≤1 h/day, particularly in semantic and phonemic fluency and pragmatic communication (p < 0.001). Educational content correlated positively with linguistic scores, whereas recreational content showed negative associations. Parental mediation emerged as a significant positive predictor. In the regression model (R² = 0.42), age (β = 0.47), parental mediation (β = 0.21), and educational content (β = 0.18) predicted better linguistic performance, while screen time (β = −0.29) predicted lower performance. Conclusions: The findings indicate that associations between digital media use and linguistic and pragmatic performance vary across developmental stages and contextual factors. Rather than screen time alone, digital content quality and parental mediation are associated with differences in communicative performance. These results highlight the value of a nuanced, developmentally informed perspective when considering children’s digital media environments. Full article

Background: Food allergy [FA] is a growing public health concern among school-age children, with schools and childcare/daycare settings representing high-risk environments for accidental exposure and anaphylaxis. Objective: To systematically review evidence on digital health interventions supporting FA education, prevention, and management in school settings. Methods: A systematic search of PubMed, Scopus, Web of Science, and CINAHL was conducted to identify studies published between January 2015 and December 2025 [PROSPERO CRD420251185553]. Eligible studies evaluated e-learning, mHealth, or web-based programs targeting school staff, parents, or students. Results: Sixteen studies met inclusion criteria. Digital health emerged as a catalyst for professional development, interprofessional communication, and health equity within school communities. Interventions consistently improved knowledge, preparedness, and self-efficacy in anaphylaxis management among school staff, strengthened parental empowerment and communication with schools, and supported coping and inclusion among allergic children. Evidence on clinical outcomes; however, remains limited. Conclusions: Digital health can meaningfully enhance school preparedness and reduce inequalities in allergy management. Integrating digital tools into national school health frameworks—particularly where school nursing is not yet institutionalized—may represent a pivotal step toward safer, more equitable inclusion of children with food allergy. Full article

Objectives: This study presents the development of a bilingual, expert-evaluated question–answer (Q&A) dataset, named PMR-Q&A, designed for training large language models (LLMs) in the field of Physical Medicine and Rehabilitation (PMR). Methods: The dataset was created through a systematic and semi-automated framework that converts unstructured scientific texts into structured Q&A pairs. Source materials included eight core reference books, 2310 academic publications, and 323 theses covering 15 disease categories commonly encountered in PMR clinical practice. Texts were digitized using layout-aware optical character recognition (OCR), semantically segmented, and distilled through a two-pass LLM strategy employing GPT-4.1 and GPT-4.1-mini models. Results: The resulting dataset consists of 143,712 bilingual Q&A pairs, each annotated with metadata including disease category, reference source, and keywords. A representative subset of 3000 Q&A pairs was extracted for expert validation to evaluate the dataset’s reliability and representativeness. Statistical analyses showed that the validation sample accurately reflected the thematic and linguistic structure of the full dataset, with an average score of 1.90. Conclusions: The PMR-Q&A dataset is a structured and expert-evaluated resource for developing and fine-tuning domain-specific large language models, supporting research and educational efforts in the field of physical medicine and rehabilitation. Full article