Search Results (2,022)

With the advent of generative AI, medical imaging has been revolutionized, allowing for unprecedented capabilities in data generation, volumetric reconstruction, and clinical decision support. Although significant advances have been achieved in two-dimensional modalities, extending them to three-dimensional medical imaging, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET), remains a developing frontier with new technical and clinical challenges. This survey offers a thorough, systematic exploration of generative AI approaches uniquely applicable to 3D medical imaging, including voxel-based generative models, implicit neural representations, and latent diffusion models. The literature is organized in three orthogonal axes: imaging modality (MRI, CT, and PET), model architecture (GAN, VAE, diffusion, and NeRF), and clinical application (augmentation, reconstruction, surgical planning, and anomaly detection). We provide detailed taxonomy tables for each axis, including landmark papers, strengths, limitations, key techniques, and benchmark performance. We also address evaluation protocols, ethical issues related to synthetic data, and open research challenges. We analyzed more than 50 representative works and found that latent diffusion models have firmly established themselves as the standard for high-fidelity 3D synthesis and that implicit neural 3D representations are best for reconstructing 3D scenes from sparse views with limited memory. It does not, however, mean that they perform better across all modalities and tasks, as they have significantly greater requirements in terms of computational and memory load, sampling time, and training data compared to alternatives like diffusion-based methods (which account for about 44% of the surveyed landmark architectures. Finally, we propose a clinical, ethical, and technically sound blueprint for the use of generative AI in volumetric medical imaging. Full article

The accelerating convergence of urbanization, climate change, and digital transformation is reshaping how cities are conceived, designed, and managed. This critical narrative review asks one central question: how can newly emerging digital capabilities and established computational and decision-support methods, when applied in novel or rapidly evolving ways, support accountable urban-design decisions for sustainable urban transitions? “Emerging” is used here selectively for capabilities characterized by recent technical development, rapid diffusion, limited implementation maturity, or new urban-design applications—not as a label for established methods such as GIS, BIM, AHP/ANP, or parametric modelling. The review is organized by functional stage in the urban-design process: (i) conceptual and technological foundations; (ii) urban analysis and systems interpretation; (iii) computational design support; (iv) decision-making and spatial intervention; and (v) outcome evaluation and feedback. Across these stages, comparative evidence is used to examine function, data requirements, spatial scale, application stage, empirical maturity, risks, and limitations. The synthesis indicates that digital and computational methods are most useful when they augment rather than displace professional and civic judgement, make trade-offs and uncertainty explicit, and connect evidence to context-sensitive spatial intervention. Urban design is therefore treated not as the sole driver of transition but as a mediating spatial practice within wider planning, governance, socio-technical, and socio-ecological processes. Full article

Orthognathic and reconstructive maxillofacial surgery addresses severe dentofacial deformities, post-traumatic defects, and defects following oncologic resection. Across the pathway from preoperative imaging and virtual planning to soft-tissue prediction and intraoperative guidance, artificial intelligence and related digital technologies are increasingly being investigated to support decision-making, simulation, and plan transfer. This structured narrative review synthesizes evidence across that surgical pipeline and uses data availability as an organizing framework for interpreting evidence maturity. Predefined searches of PubMed/MEDLINE, Embase, and Scopus identified English-language peer-reviewed articles published from January 2018 to April 2026, supplemented by selected foundational studies. Within the literature reviewed, automated cephalometric and three-dimensional landmark detection has undergone the most extensive quantitative evaluation, supported by several systematic reviews and meta-analyses and by multicenter retrospective evaluations, including one study with independent external test sets. Artificial intelligence-assisted diagnosis, osteotomy planning, and virtual surgical planning show increasing technical capability, including low-millimeter reposition-vector prediction, although external validation and patient-centered outcomes remain limited. Soft-tissue prediction has advanced through deep learning and finite-element modeling, with selected studies reporting comparable geometric accuracy and substantially faster computation. In contrast, intraoperative augmented reality, navigation, and robot-assisted craniomaxillofacial surgery remain supported mainly by small clinical series, cadaveric studies, and preclinical validation. These technologies demonstrate plan-transfer feasibility in selected settings but do not yet establish broad clinical effectiveness or routine superiority over established workflows. Overall, evidence is more mature when datasets are structured, accessible, and standardized, and less mature when paired longitudinal imaging or intraoperative tracking data are sparse. This association should be interpreted as an organizing hypothesis rather than proof of causality. Full article

Automated assessment of programming assignments remains a major challenge in higher education, particularly in large-scale courses where timely, consistent, and pedagogically meaningful feedback is difficult to provide, while Large Language Models (LLMs) have shown strong capabilities in code understanding and feedback generation, their use as standalone evaluators is fundamentally limited by inconsistency, lack of transparency, and weak alignment with instructional objectives. This paper argues that these limitations are not intrinsic to LLMs, but rather arise from their deployment as isolated components. In response, we propose a system-centric approach to AI-assisted assessment, introducing a hybrid framework that integrates LLMs within a structured, context-aware, and pedagogically aligned evaluation pipeline. The framework combines (i) explicit rubric-based decomposition of evaluation criteria, (ii) pedagogically guided prompting, and (iii) Retrieval-Augmented Generation (RAG) grounded in course-specific materials. Together, these components transform the evaluation process from a black-box prediction task into a traceable and reproducible decision process. The proposed approach is implemented in a real-world educational platform, EvaluaTeC, and evaluated on a dataset of 1287 programming submissions from 429 students. Experimental results show that the hybrid framework improves agreement with consolidated instructor reference grades ($r=0.9059$ vs. $0.7207$ baseline), reduces evaluation error (MAE = 0.5134), and exhibited lower output variability in the recorded aggregate statistics, while maintaining practical latency and cost. Beyond numerical improvements, the system approximates key statistical properties of human grading and generates structured, pedagogically aligned feedback. These findings demonstrate that reliable AI-assisted assessment emerges from the integration of LLMs within structured and context-aware systems, rather than from model capabilities alone. This work contributes a principled framework for explainable and scalable automated assessment, advancing the design of trustworthy AI systems in education. This shift reframes automated assessment as a systems problem rather than a purely model-centric task. Full article

Soft tissue deficiencies around teeth and dental implants remain a significant challenge in contemporary periodontology and implant dentistry. These deficiencies influence a number of factors, including esthetic outcomes, peri-implant tissue stability, long-term maintenance, and patient satisfaction. Although autogenous connective tissue grafts remain the clinical gold standard for soft tissue augmentation, their use is limited by donor-site morbidity, increased surgical time, and restricted tissue availability. Consequently, polymer-based biomaterials have emerged as promising alternatives or adjunctive regenerative materials capable of improving wound healing while reducing surgical invasiveness. This narrative review compares natural polymers, including collagen, hyaluronic acid, extracellular matrix-derived scaffolds, fibrin-based platelet concentrates, and gelatin-based materials, with synthetic polymeric systems, such as polycaprolactone, polylactic acid, polyethylene glycol, and advanced composite hydrogels. The influence of these materials on angiogenesis, fibroblast migration, extracellular matrix remodeling, immune modulation, and soft tissue integration is reviewed in the context of periodontal plastic surgery and peri-implant soft tissue reconstruction. This narrative review summarizes the current landscape of polymer-based biomaterials used for periodontal and peri-implant soft tissue augmentation, integrating material science with clinical evidence. The advantages, limitations, indications and future perspectives of polymer-based substitutes are discussed in comparison with autogenous grafting procedures. Full article

Elevated levels of reactive oxygen species (ROS) and persistent inflammatory responses represent principal impediments to efficacious wound healing, particularly in mechanically dynamic or infected wound environments. Consequently, multifunctional hydrogel-based dressings capable of integrating coordinated antioxidant, anti-inflammatory, and antimicrobial activities with robust wet-tissue adhesion are highly sought after, yet their development remains limited. Herein, we introduce a curcumin-functionalized polyurethane composite dressing (designated CPFSD), engineered through the incorporation of dopamine-grafted sodium alginate (SD) and ferrocene (Fc), which collectively confer synergistic ROS modulation alongside durable tissue adhesion. Mechanistically, curcumin provides intrinsic antioxidant and anti-inflammatory properties, while Fc facilitates reversible Fe²⁺/Fe³⁺ redox cycling, thereby augmenting ROS scavenging capacity; concurrently, catechol and hydroquinone moieties present on SD establish stable interfacial interactions with moist biological tissues. Comprehensive physicochemical characterization revealed that CPFSD effectively scavenged over 80% of both DPPH and hydroxyl (·OH) radicals, exhibited greater than 80% antibacterial efficacy against Escherichia coli and Staphylococcus aureus, and demonstrated markedly enhanced adhesive performance while preserving mechanical flexibility and cytocompatibility. In vitro assays further indicated that CPFSD significantly attenuated oxidative stress in L929 fibroblasts and RAW264.7 macrophages, accompanied by downregulation of pro-inflammatory cytokine expression. In a murine full-thickness excisional wound model, CPFSD facilitated accelerated epithelialization, angiogenesis, and wound contraction, achieving wound closure at day 11 without observable systemic toxicity. Collectively, these findings underscore that rational multicomponent design strategies can yield adhesion-capable wound dressings endowed with synergistic therapeutic functionalities suitable for addressing complex pathological wound conditions. Full article

Accurate road graph extraction from satellite imagery is essential for large-scale mapping and geospatial analysis. Recent one-shot graph extraction frameworks based on foundation models have achieved promising performance, but their effectiveness decreases in complex environments where road structures are partially obscured by vegetation, buildings, shadows, and other surface conditions. These occlusion-induced disturbances lead to incomplete connectivity and degraded topology reconstruction, particularly under out-of-domain scenarios. This study proposes an occlusion-aware refinement framework to improve the robustness of satellite image road graph extraction while maintaining the original backbone architecture. The proposed framework introduces three complementary strategies: Synthetic Occlusion Augmentation for explicit occlusion-aware representation learning, an Occlusion-Adaptive Extended-Line strategy with Hard-Mining Topology Optimization for improved connectivity reasoning, and an Occlusion-Adaptive Node-Guided Resampling mechanism for reliable graph node localization. Experiments conducted on the Global-Scale road graph extraction benchmark demonstrate that the proposed method consistently improves topology reconstruction performance. Compared with the reproduced SAM-Road++ baseline, the proposed framework improves TOPO F1 from 61.81 to 62.56 on the in-domain split and from 46.93 to 51.51 on the out-of-domain split. Furthermore, the ID-OOD performance gap is reduced from 14.88 to 11.05, indicating enhanced robustness under unseen geographic conditions. The results demonstrate that explicitly modeling occlusion as a structured factor can effectively improve the generalization capability of satellite road graph extraction systems. Full article

Recent advances have been achieved in intelligent fault diagnosis of rolling bearings. However, noise interference and data imbalance remain critical challenges for achieving accurate and reliable fault diagnosis in practical industrial applications. To address the limited robustness of conventional deep learning models under noisy conditions and their bias toward majority classes in imbalanced scenarios, this study proposes a robust bearing fault diagnosis method based on an adaptive weighted heterogeneous ensemble learning framework. The proposed method begins with continuous wavelet transform (CWT), which is employed to preprocess raw vibration signals and convert them into time–frequency images. Subsequently, a residual convolutional denoising autoencoder augmented by the convolutional block attention module is developed, namely CBAM-RCDAE. CBAM-RCDAE is capable of effectively reducing and eliminating noise interference in two-dimensional image data, thus enhancing fault diagnosis accuracy. Furthermore, a heterogeneous ensemble learning framework consisting of three base learners, including Swin Transformer, a multi-scale convolutional neural network, and BiLSTM, is developed to enhance generalization capability. An adaptive weight selection (AWS) strategy is introduced to adjust the weights and aggregate the outputs of the three base learners for final fault classification. The proposed method is extensively evaluated on the PU and CWRU bearing datasets. Experimental results demonstrate that, under the most challenging imbalanced conditions, the proposed method improves the G-mean metric by 5.89% and 4.95% compared with the state-of-the-art methods on the PU and CWRU datasets, respectively. In addition, the proposed method exhibits superior noise robustness, enabling reliable fault diagnosis performance across various noise levels. Full article

The data-driven solutions of Industry 4.0 have affected the role of human factors and ergonomics (HFE) in blending natural and artificial environments in the military to train aircrews for emergency situations. This shift, through the widespread deployment of digitalization and cyber–physical systems, has led to the implementation of engineered autonomy that can predicate rational, goal-driven decisions. The extensible architecture of a fifth-generation aircraft with autonomous sensor management places the human–autonomy team at the center of collaborative sensing operations, shared verification and proactive control for risk mitigation. In the pursuit of aircraft safety during a mission, trust calibration between pilots and intelligent agents (IA) in safety-critical aviation environments remains poorly understood. Existing models do not account for how personality influences human–agent trust. Consequently, a Personality Gap Model (PGM), based on Jung’s functions-attitude is proposed to characterize trust under Partial Mission Capable (PMC) status. This conceptual relationship unfolds through the interactions of processes that need to work jointly, as Prof. James Reason emphasized in his Swiss Cheese Model (SCM). With salience information, these elements enable HFE practitioners to model the human personality structure embedded in the SCM, thereby augmenting a Trust Calibration Space (TCS) for designing trust-aware interventions. Full article

Interaction with physical objects plays a key role in the development, assessment, and treatment of cognitive and motor skills across the lifespan. In recent years, this interaction has been enhanced by smart objects, namely everyday items augmented with embedded sensing, processing, and feedback capabilities. These objects enable objective behavioural data collection while preserving natural and engaging human–object interactions. However, the literature remains fragmented and often focused on specific applications or interaction paradigms. This scoping review provides an overview of smart objects developed for clinical and educational contexts, with intended uses including assessment, treatment, training, education, and data collection to support machine learning approaches. Forty-two studies published from 2010 up to the final search date of 29 June 2026 were analysed following the PRISMA-ScR guidelines. The review adopts a design- and hardware-oriented perspective, classifying smart objects according to physical shape, intended use, target population, and validation level. Particular attention is given to embedded electronic components, measured parameters, and the exploitation of sensing and feedback technologies during human–object interaction. By synthesising current solutions, this review highlights emerging trends, recurring limitations, and open challenges related to design choices, technological constraints, and experimental validation, supporting the development of robust, adaptable, and real-world-ready smart objects. Full article

Generative artificial intelligence (GenAI) is reshaping higher education and early-career work, raising questions about how universities can support pedagogically sustainable career readiness. This study examines undergraduate students’ perceptions of AI, automation, entry-level employment, and perceived preparedness for an AI-augmented labor market. Survey data were collected from 153 undergraduate students. The questionnaire examined awareness of AI and automation, perceived risks to traditional entry-level work, anxiety and perceived preparedness regarding post-graduation employment, skill priorities, and desired institutional support. The findings indicate substantial awareness of AI-related change, with many students expecting routine junior tasks such as data entry, basic research, report generation, customer support, and simple coding-related work to be affected. However, confidence in academic preparation was weaker and more uncertain. Students emphasized human-centered capabilities, including critical thinking, creativity, communication, and problem solving, alongside AI literacy and practical exposure to digital tools. The study identifies an awareness-preparedness gap and argues that higher education institutions should strengthen GenAI-era curriculum design, AI-authentic assessment, experiential learning, career guidance, and ethical AI literacy to support perceived preparedness and sustainable career readiness. Full article

Emerging-market exporters encounter global sustainability governance not as a treaty or a target but as a product rule: an energy performance threshold, a restricted-substance limit, and a recyclability declaration. Those rules are published, and publication is not the same as access. A firm with a regulatory affairs department can establish which version of a measure is in force, which products it covers and what evidence the destination market will accept; the small- and medium-sized enterprises that dominate emerging-market export bases usually cannot, so rules written to raise environmental standards can exclude the firms least equipped to read them. This paper asks whether emerging digital technologies can convert the transparency infrastructure of the trading system into sustainable business capability. We develop GRACE, a WTO-informed decision-support system that resolves regulatory versions and timelines before interpretation begins, ranks evidence by the authority of its source, declines to state any obligation that no official passage supports, and escalates to human experts when the record is incomplete. Evaluation covers 214 held-out notification families under family-level splits and five seeds, against direct prompting, generic retrieval-augmented generation, a domain-adapted single agent with the same retriever and supervision, an always-on version of the same agent set, a hierarchical-audit baseline and a frozen frontier model, with 72 cases scored blind by trade-law assessors. Against the domain-adapted single agent, it raises citation support from 87.4% to 92.6%, a 5.2-point gain (95% bootstrap CI for the difference [3.6, 6.9]), lifts requirement-action coverage from 82.9% to 88.4%, reduces unsupported claims by 45.3%, and matches the always-on pipeline at 36.7% fewer tokens; blind expert scores are 4.16 of 5 against 3.60. Within the evaluated stack, interpretive capacity, not information supply, is what converts environmental regulation into sustainable business practice. Full article

Multi-modal MRI provides essential anatomical and pathological information for accurate brain tumor segmentation. However, deep learning-based segmentation methods are hampered by limited annotated data and incomplete modality acquisition in clinical MRI datasets. To address this issue, we propose a synthetic enhancement framework based on SCGAN-MultiJNet for multi-modal brain-tumor MRI. Specifically, SCGAN establishes a dual-branch disentangled latent space to independently encode images’ structural contour and textural features. Combined with the multi-scale fusion capability of MultiJNet, the proposed network realizes effective modality translation. Subsequently, synthetic samples are mixed with BraTS2020 training data to optimize the U-Net segmentation model. With the optimal real–synthetic data-mixing strategy, the segmentation metrics are improved: accuracy, Dice, precision, and IoU increase from 0.9857, 0.8919, 0.8980, and 0.8054 to 0.9864, 0.8962, 0.9205, and 0.8122. The proposed synthetic augmentation experimentally demonstrates enhancements in model robustness against MRI disturbances (including Gaussian blur, brightness shift, etc.). Finally, cross-domain generalization experiments conducted on the BraTS2025-SSA-Data demonstrate that introducing synthetic data augmentation can effectively boost the model’s cross-domain generalization capability. Overall, the proposed SCGAN-MultiJNet-based data synthesis algorithm provides a feasible technical solution to break the data bottleneck in multi-modal MRI brain tumor segmentation. Full article

Oriented object detection in Synthetic Aperture Radar (SAR) imagery plays an important role in remote sensing, but its performance is usually limited by the shortage of high-quality annotated samples. This problem is particularly prominent in multi-class scenarios, where different targets exhibit significantly different scattering characteristics, scale distributions, orientation variations, and background dependencies. Existing SAR sample synthesis methods are mostly designed for single-category targets or horizontal bounding box constraints, and suffer from insufficient category diversity, weak orientation controllability, and inadequate modeling of geo-topological relationships. To address these problems, this paper proposes the first diffusion-based sample generation method for multi-class SAR oriented object detection. A large-scale SAR image-geo-topological semantic text paired dataset is constructed, and a SAR text-to-image foundation model is pretrained based on Stable Diffusion, enabling the model to learn target categories, quantities, spatial distributions, and geo-topological relationships, thereby improving the geographic plausibility and scene consistency of generated results. Furthermore, a Direction Phase Shifting encoding strategy is proposed to alleviate the boundary discontinuity problem in rotation-angle representation and to achieve precise control of target location, scale, and orientation based on oriented bounding boxes. Meanwhile, an automated sample generation and label refinement pipeline is designed. More accurate oriented bounding boxes that better fit target contours are obtained through wavelet denoising and progressive SAM-2 segmentation, improving the annotation accuracy of synthetic samples. Experimental results show that our method can produce SAR images with diverse scattering characteristics, realistic background variations, and reasonable geo-topological relationships. The generated samples consistently improve detection performance on five baseline oriented object detectors, providing an effective data augmentation strategy for enhancing the robustness and generalization capability of SAR oriented object detection models. Full article

Operational redundancies in Nigeria’s airport logistics, including duplicated clearance procedures, fragmented data systems, and repeated physical inspections, generate substantial inefficiencies that hamper service delivery, increase costs, and raise sustainability concerns. Despite growing interest in blockchain technology (BCT) as a logistics innovation, empirical evidence from Sub-Saharan African aviation contexts is largely limited. This study examines how stakeholders perceive BCT as a potential mechanism for reducing procedural, informational, and structural redundancies across Nigerian airport operations. A qualitative research design was employed, drawing on 45 semi-structured interviews with airport managers, airline officials, regulatory bodies, and aviation experts. Data were analyzed using Leximancer, yielding eight thematic clusters across 115 concepts. To augment thematic rigor, three quantitative measures—Normalized Pointwise Mutual Information (NPMI), Jaccard similarity, and degree centrality—were applied to the exported Concept Co-occurrence Matrix (CCMatrix; 6555 pairwise relationships). The blockchain theme exhibited the highest intra-theme cohesion (mean NPMI = 0.615) and the second highest normalized centrality (0.638), suggesting that it functions as a semantically tight, cross-cutting concept in participants’ discourse. The strongest concept pair, inefficiency–reliability (NPMI = 0.989; Jaccard = 0.909), is consistent with participants’ recurring association of service failures with procedurally generated redundancies. Stakeholders perceived BCT as potentially addressing these redundancies through shared ledger architectures, smart contract automation, and consolidated identity management, with anticipated sustainability co-benefits across energy, documentation and processing domains. The study offers an exploratory, contextualized application of the Technology–Organization–Environment (TOE) framework, the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Dynamic Capabilities Framework (DCF) to a resource-constrained developing-country aviation context and proposes a stakeholder-informed, phased BCT adoption framework—not yet tested through implementation—with direct policy and managerial implications for the Nigerian airport administration. Full article