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Search Results (687)

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Keywords = fidelity assessment

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25 pages, 6934 KiB  
Article
Feature Constraints Map Generation Models Integrating Generative Adversarial and Diffusion Denoising
by Chenxing Sun, Xixi Fan, Xiechun Lu, Laner Zhou, Junli Zhao, Yuxuan Dong and Zhanlong Chen
Remote Sens. 2025, 17(15), 2683; https://doi.org/10.3390/rs17152683 - 3 Aug 2025
Viewed by 157
Abstract
The accelerated evolution of remote sensing technology has intensified the demand for real-time tile map generation, highlighting the limitations of conventional mapping approaches that rely on manual cartography and field surveys. To address the critical need for rapid cartographic updates, this study presents [...] Read more.
The accelerated evolution of remote sensing technology has intensified the demand for real-time tile map generation, highlighting the limitations of conventional mapping approaches that rely on manual cartography and field surveys. To address the critical need for rapid cartographic updates, this study presents a novel multi-stage generative framework that synergistically integrates Generative Adversarial Networks (GANs) with Diffusion Denoising Models (DMs) for high-fidelity map generation from remote sensing imagery. Specifically, our proposed architecture first employs GANs for rapid preliminary map generation, followed by a cascaded diffusion process that progressively refines topological details and spatial accuracy through iterative denoising. Furthermore, we propose a hybrid attention mechanism that strategically combines channel-wise feature recalibration with coordinate-aware spatial modulation, enabling the enhanced discrimination of geographic features under challenging conditions involving edge ambiguity and environmental noise. Quantitative evaluations demonstrate that our method significantly surpasses established baselines in both structural consistency and geometric fidelity. This framework establishes an operational paradigm for automated, rapid-response cartography, demonstrating a particular utility in time-sensitive applications including disaster impact assessment, unmapped terrain documentation, and dynamic environmental surveillance. Full article
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19 pages, 1107 KiB  
Article
A Novel Harmonic Clocking Scheme for Concurrent N-Path Reception in Wireless and GNSS Applications
by Dina Ibrahim, Mohamed Helaoui, Naser El-Sheimy and Fadhel Ghannouchi
Electronics 2025, 14(15), 3091; https://doi.org/10.3390/electronics14153091 - 1 Aug 2025
Viewed by 216
Abstract
This paper presents a novel harmonic-selective clocking scheme that facilitates concurrent downconversion of spectrally distant radio frequency (RF) signals using a single low-frequency local oscillator (LO) in an N-path receiver architecture. The proposed scheme selectively generates LO harmonics aligned with multiple RF bands, [...] Read more.
This paper presents a novel harmonic-selective clocking scheme that facilitates concurrent downconversion of spectrally distant radio frequency (RF) signals using a single low-frequency local oscillator (LO) in an N-path receiver architecture. The proposed scheme selectively generates LO harmonics aligned with multiple RF bands, enabling simultaneous downconversion without modification of the passive mixer topology. The receiver employs a 4-path passive mixer configuration to enhance harmonic selectivity and provide flexible frequency planning.The architecture is implemented on a printed circuit board (PCB) and validated through comprehensive simulation and experimental measurements under continuous wave and modulated signal conditions. Measured results demonstrate a sensitivity of 55dBm and a conversion gain varying from 2.5dB to 9dB depending on the selected harmonic pair. The receiver’s performance is further corroborated by concurrent (dual band) reception of real-world signals, including a GPS signal centered at 1575 MHz and an LTE signal at 1179 MHz, both downconverted using a single 393 MHz LO. Signal fidelity is assessed via Normalized Mean Square Error (NMSE) and Error Vector Magnitude (EVM), confirming the proposed architecture’s effectiveness in maintaining high-quality signal reception under concurrent multiband operation. The results highlight the potential of harmonic-selective clocking to simplify multiband receiver design for wireless communication and global navigation satellite system (GNSS) applications. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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22 pages, 3527 KiB  
Review
Applications of Organoids and Spheroids in Anaplastic and Papillary Thyroid Cancer Research: A Comprehensive Review
by Deepak Gulwani, Neha Singh, Manisha Gupta, Ridhima Goel and Thoudam Debraj Singh
Organoids 2025, 4(3), 18; https://doi.org/10.3390/organoids4030018 - 1 Aug 2025
Viewed by 98
Abstract
Organoid and spheroid technologies have rapidly become pivotal in thyroid cancer research, offering models that are more physiologically relevant than traditional two-dimensional culture. In the study of papillary and anaplastic thyroid carcinomas, two subtypes that differ both histologically and clinically, three-dimensional (3D) models [...] Read more.
Organoid and spheroid technologies have rapidly become pivotal in thyroid cancer research, offering models that are more physiologically relevant than traditional two-dimensional culture. In the study of papillary and anaplastic thyroid carcinomas, two subtypes that differ both histologically and clinically, three-dimensional (3D) models offer unparalleled insights into tumor biology, therapeutic vulnerabilities, and resistance mechanisms. These models maintain essential tumor characteristics such as cellular diversity, spatial structure, and interactions with the microenvironment, making them extremely valuable for disease modeling and drug testing. This review emphasizes recent progress in the development and use of thyroid cancer organoids and spheroids, focusing on their role in replicating disease features, evaluating targeted therapies, and investigating epithelial–mesenchymal transition (EMT), cancer stem cell behavior, and treatment resistance. Patient-derived organoids have shown potential in capturing individualized drug responses, supporting precision oncology strategies for both differentiated and aggressive subtypes. Additionally, new platforms, such as thyroid organoid-on-a-chip systems, provide dynamic, high-fidelity models for functional studies and assessments of endocrine disruption. Despite ongoing challenges, such as standardization, limited inclusion of immune and stromal components, and culture reproducibility, advancements in microfluidics, biomaterials, and machine learning have enhanced the clinical and translational potential of these systems. Organoids and spheroids are expected to become essential in the future of thyroid cancer research, particularly in bridging the gap between laboratory discoveries and patient-focused therapies. Full article
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20 pages, 2981 KiB  
Article
Data-Driven Modelling and Simulation of Fuel Cell Hybrid Electric Powertrain
by Mehroze Iqbal, Amel Benmouna and Mohamed Becherif
Hydrogen 2025, 6(3), 53; https://doi.org/10.3390/hydrogen6030053 - 1 Aug 2025
Viewed by 106
Abstract
Inspired by the Toyota Mirai, this study presents a high-fidelity data-driven approach for modelling and simulation of a fuel cell hybrid electric powertrain. This study utilises technical assessment data sourced from Argonne National Laboratory’s publicly available report, faithfully modelling most of the vehicle [...] Read more.
Inspired by the Toyota Mirai, this study presents a high-fidelity data-driven approach for modelling and simulation of a fuel cell hybrid electric powertrain. This study utilises technical assessment data sourced from Argonne National Laboratory’s publicly available report, faithfully modelling most of the vehicle subsystems as data-driven entities. The simulation framework is developed in the MATLAB/Simulink environment and is based on a power dynamics approach, capturing nonlinear interactions and performance intricacies between different powertrain elements. This study investigates subsystem synergies and performance boundaries under a combined driving cycle composed of the NEDC, WLTP Class 3 and US06 profiles, representing urban, extra-urban and aggressive highway conditions. To emulate the real-world load-following strategy, a state transition power management and allocation method is synthesised. The proposed method dynamically governs the power flow between the fuel cell stack and the traction battery across three operational states, allowing the battery to stay within its allocated bounds. This simulation framework offers a near-accurate and computationally efficient digital counterpart to a commercial hybrid powertrain, serving as a valuable tool for educational and research purposes. Full article
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20 pages, 586 KiB  
Article
Implementing High-Intensity Gait Training in Stroke Rehabilitation: A Real-World Pragmatic Approach
by Jennifer L. Moore, Pia Krøll, Håvard Hansen Berg, Merethe B. Sinnes, Roger Arntsen, Chris E. Henderson, T. George Hornby, Stein Arne Rimehaug, Ingvild Lilleheie and Anders Orpana
J. Clin. Med. 2025, 14(15), 5409; https://doi.org/10.3390/jcm14155409 - 31 Jul 2025
Viewed by 281
Abstract
Background: High-intensity gait training (HIT) is an evidence-based intervention recommended for stroke rehabilitation; however, its implementation in routine practice is inconsistent. This study examined the real-world implementation of HIT in an inpatient rehabilitation setting in Norway, focusing on fidelity, barriers, and knowledge [...] Read more.
Background: High-intensity gait training (HIT) is an evidence-based intervention recommended for stroke rehabilitation; however, its implementation in routine practice is inconsistent. This study examined the real-world implementation of HIT in an inpatient rehabilitation setting in Norway, focusing on fidelity, barriers, and knowledge translation (KT) strategies. Methods: Using the Knowledge-to-Action (KTA) framework, HIT was implemented in three phases: pre-implementation, implementation, and competency. Fidelity metrics and coverage were assessed in 99 participants post-stroke. Barriers and facilitators were documented and categorized using the Consolidated Framework for Implementation Research. Results: HIT was delivered with improved fidelity during the implementation and competency phases, reflected by increased stepping and heart rate metrics. A coverage rate of 52% was achieved. Barriers evolved over time, beginning with logistical and knowledge challenges and shifting toward decision-making complexity. The KT interventions, developed collaboratively by clinicians and external facilitators, supported implementation. Conclusions: Structured pre-implementation planning, clinician engagement, and external facilitation enabled high-fidelity HIT implementation in a real-world setting. Pragmatic, context-sensitive strategies were critical to overcoming evolving barriers. Future research should examine scalable, adaptive KT strategies that balance theoretical guidance with clinical feasibility to sustain evidence-based practice in rehabilitation. Full article
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12 pages, 3315 KiB  
Article
NeRF-RE: An Improved Neural Radiance Field Model Based on Object Removal and Efficient Reconstruction
by Ziyang Li, Yongjian Huai, Qingkuo Meng and Shiquan Dong
Information 2025, 16(8), 654; https://doi.org/10.3390/info16080654 - 31 Jul 2025
Viewed by 145
Abstract
High-quality green gardens can markedly enhance the quality of life and mental well-being of their users. However, health and lifestyle constraints make it difficult for people to enjoy urban gardens, and traditional methods struggle to offer the high-fidelity experiences they need. This study [...] Read more.
High-quality green gardens can markedly enhance the quality of life and mental well-being of their users. However, health and lifestyle constraints make it difficult for people to enjoy urban gardens, and traditional methods struggle to offer the high-fidelity experiences they need. This study introduces a 3D scene reconstruction and rendering strategy based on implicit neural representation through the efficient and removable neural radiation fields model (NeRF-RE). Leveraging neural radiance fields (NeRF), the model incorporates a multi-resolution hash grid and proposal network to improve training efficiency and modeling accuracy, while integrating a segment-anything model to safeguard public privacy. Take the crabapple tree, extensively utilized in urban garden design across temperate regions of the Northern Hemisphere. A dataset comprising 660 images of crabapple trees exhibiting three distinct geometric forms is collected to assess the NeRF-RE model’s performance. The results demonstrated that the ‘harvest gold’ crabapple scene had the highest reconstruction accuracy, with PSNR, LPIPS and SSIM of 24.80 dB, 0.34 and 0.74, respectively. Compared to the Mip-NeRF 360 model, the NeRF-RE model not only showed an up to 21-fold increase in training efficiency for three types of crabapple trees, but also exhibited a less pronounced impact of dataset size on reconstruction accuracy. This study reconstructs real scenes with high fidelity using virtual reality technology. It not only facilitates people’s personal enjoyment of the beauty of natural gardens at home, but also makes certain contributions to the publicity and promotion of urban landscapes. Full article
(This article belongs to the Special Issue Extended Reality and Its Applications)
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16 pages, 628 KiB  
Article
Beyond the Bot: A Dual-Phase Framework for Evaluating AI Chatbot Simulations in Nursing Education
by Phillip Olla, Nadine Wodwaski and Taylor Long
Nurs. Rep. 2025, 15(8), 280; https://doi.org/10.3390/nursrep15080280 - 31 Jul 2025
Viewed by 225
Abstract
Background/Objectives: The integration of AI chatbots in nursing education, particularly in simulation-based learning, is advancing rapidly. However, there is a lack of structured evaluation models, especially to assess AI-generated simulations. This article introduces the AI-Integrated Method for Simulation (AIMS) evaluation framework, a dual-phase [...] Read more.
Background/Objectives: The integration of AI chatbots in nursing education, particularly in simulation-based learning, is advancing rapidly. However, there is a lack of structured evaluation models, especially to assess AI-generated simulations. This article introduces the AI-Integrated Method for Simulation (AIMS) evaluation framework, a dual-phase evaluation framework adapted from the FAITA model, designed to evaluate both prompt design and chatbot performance in the context of nursing education. Methods: This simulation-based study explored the application of an AI chatbot in an emergency planning course. The AIMS framework was developed and applied, consisting of six prompt-level domains (Phase 1) and eight performance criteria (Phase 2). These domains were selected based on current best practices in instructional design, simulation fidelity, and emerging AI evaluation literature. To assess the chatbots educational utility, the study employed a scoring rubric for each phase and incorporated a structured feedback loop to refine both prompt design and chatbox interaction. To demonstrate the framework’s practical application, the researchers configured an AI tool referred to in this study as “Eval-Bot v1”, built using OpenAI’s GPT-4.0, to apply Phase 1 scoring criteria to a real simulation prompt. Insights from this analysis were then used to anticipate Phase 2 performance and identify areas for improvement. Participants (three individuals)—all experienced healthcare educators and advanced practice nurses with expertise in clinical decision-making and simulation-based teaching—reviewed the prompt and Eval-Bot’s score to triangulate findings. Results: Simulated evaluations revealed clear strengths in the prompt alignment with course objectives and its capacity to foster interactive learning. Participants noted that the AI chatbot supported engagement and maintained appropriate pacing, particularly in scenarios involving emergency planning decision-making. However, challenges emerged in areas related to personalization and inclusivity. While the chatbot responded consistently to general queries, it struggled to adapt tone, complexity and content to reflect diverse learner needs or cultural nuances. To support replication and refinement, a sample scoring rubric and simulation prompt template are provided. When evaluated using the Eval-Bot tool, moderate concerns were flagged regarding safety prompts and inclusive language, particularly in how the chatbot navigated sensitive decision points. These gaps were linked to predicted performance issues in Phase 2 domains such as dialog control, equity, and user reassurance. Based on these findings, revised prompt strategies were developed to improve contextual sensitivity, promote inclusivity, and strengthen ethical guidance within chatbot-led simulations. Conclusions: The AIMS evaluation framework provides a practical and replicable approach for evaluating the use of AI chatbots in simulation-based education. By offering structured criteria for both prompt design and chatbot performance, the model supports instructional designers, simulation specialists, and developers in identifying areas of strength and improvement. The findings underscore the importance of intentional design, safety monitoring, and inclusive language when integrating AI into nursing and health education. As AI tools become more embedded in learning environments, this framework offers a thoughtful starting point for ensuring they are applied ethically, effectively, and with learner diversity in mind. Full article
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17 pages, 920 KiB  
Article
Enhancing Early GI Disease Detection with Spectral Visualization and Deep Learning
by Tsung-Jung Tsai, Kun-Hua Lee, Chu-Kuang Chou, Riya Karmakar, Arvind Mukundan, Tsung-Hsien Chen, Devansh Gupta, Gargi Ghosh, Tao-Yuan Liu and Hsiang-Chen Wang
Bioengineering 2025, 12(8), 828; https://doi.org/10.3390/bioengineering12080828 - 30 Jul 2025
Viewed by 424
Abstract
Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision [...] Read more.
Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision Enhancer (SAVE), an innovative, software-driven framework that transforms standard WLI into high-fidelity hyperspectral imaging (HSI) and simulated narrow-band imaging (NBI) without any hardware modification. SAVE leverages advanced spectral reconstruction techniques, including Macbeth Color Checker-based calibration, principal component analysis (PCA), and multivariate polynomial regression, achieving a root mean square error (RMSE) of 0.056 and structural similarity index (SSIM) exceeding 90%. Trained and validated on the Kvasir v2 dataset (n = 6490) using deep learning models like ResNet-50, ResNet-101, EfficientNet-B2, both EfficientNet-B5 and EfficientNetV2-B0 were used to assess diagnostic performance across six key GI conditions. Results demonstrated that SAVE enhanced imagery and consistently outperformed raw WLI across precision, recall, and F1-score metrics, with EfficientNet-B2 and EfficientNetV2-B0 achieving the highest classification accuracy. Notably, this performance gain was achieved without the need for specialized imaging hardware. These findings highlight SAVE as a transformative solution for augmenting GI diagnostics, with the potential to significantly improve early detection, streamline clinical workflows, and broaden access to advanced imaging especially in resource constrained settings. Full article
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18 pages, 2238 KiB  
Article
Dispersal Patterns of Euphydryas aurinia provincialis (Lepidoptera: Nymphalidae) in the Colfiorito Highlands, Central Italy
by Andrea Brusaferro, Silvia Marinsalti, Federico Maria Tardella, Emilio Insom and Antonietta La Terza
Environments 2025, 12(8), 263; https://doi.org/10.3390/environments12080263 - 30 Jul 2025
Viewed by 210
Abstract
We investigated the dispersal ability of Euphydryas aurinia provincialis in a local-scale analysis within a single habitat patch of the Colfiorito highlands metapopulation. Our findings indicate that inside a single node, the organization of nesting patches can be conceptualized as a metapopulation itself, [...] Read more.
We investigated the dispersal ability of Euphydryas aurinia provincialis in a local-scale analysis within a single habitat patch of the Colfiorito highlands metapopulation. Our findings indicate that inside a single node, the organization of nesting patches can be conceptualized as a metapopulation itself, where reproductive sites, despite their spatial proximity, can act as either source or sink habitats depending on environmental conditions. We conducted fieldwork in six nesting patches inside a single node, capturing, marking, and recapturing individuals to assess their spatial distribution and movement tendencies at a large landscape scale. We found a high degree of site fidelity among individuals, with many recaptures occurring within the original marking site, but also a sex-based difference in movement patterns; females dispersed farther than males, likely driven by reproductive strategies, while males remained more localized, prioritizing mate-searching. Our findings suggest a complex dynamic in habitat connectivity: pastures and abandoned fields, despite being open, seem to act like sink areas, while breeding sites with shrub and tree cover act as source habitats, offering optimal conditions for reproduction. Individuals, especially females, from these source areas were later compelled to disperse into open habitats, highlighting a nuanced interaction between landscape structure and population dynamics. These results highlight the importance of maintaining habitat corridors to support metapopulation dynamics and prevent genetic isolation; the abandonment of traditional grazing practices is leading to the rapid closure of these source habitats, posing a severe risk of local extinction. Conservation efforts should prioritize the preservation of these source habitats to ensure the long-term viability of E. a. provincialis populations in fragmented landscapes. Full article
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13 pages, 3685 KiB  
Article
A Controlled Variation Approach for Example-Based Explainable AI in Colorectal Polyp Classification
by Miguel Filipe Fontes, Alexandre Henrique Neto, João Dallyson Almeida and António Trigueiros Cunha
Appl. Sci. 2025, 15(15), 8467; https://doi.org/10.3390/app15158467 (registering DOI) - 30 Jul 2025
Viewed by 189
Abstract
Medical imaging is vital for diagnosing and treating colorectal cancer (CRC), a leading cause of mortality. Classifying colorectal polyps and CRC precursors remains challenging due to operator variability and expertise dependence. Deep learning (DL) models show promise in polyp classification but face adoption [...] Read more.
Medical imaging is vital for diagnosing and treating colorectal cancer (CRC), a leading cause of mortality. Classifying colorectal polyps and CRC precursors remains challenging due to operator variability and expertise dependence. Deep learning (DL) models show promise in polyp classification but face adoption barriers due to their ‘black box’ nature, limiting interpretability. This study presents an example-based explainable artificial intehlligence (XAI) approach using Pix2Pix to generate synthetic polyp images with controlled size variations and LIME to explain classifier predictions visually. EfficientNet and Vision Transformer (ViT) were trained on datasets of real and synthetic images, achieving strong baseline accuracies of 94% and 96%, respectively. Image quality was assessed using PSNR (18.04), SSIM (0.64), and FID (123.32), while classifier robustness was evaluated across polyp sizes. Results show that Pix2Pix effectively controls image attributes like polyp size despite limitations in visual fidelity. LIME integration revealed classifier vulnerabilities, underscoring the value of complementary XAI techniques. This enhances DL model interpretability and deepens understanding of their behaviour. The findings contribute to developing explainable AI tools for polyp classification and CRC diagnosis. Future work will improve synthetic image quality and refine XAI methodologies for broader clinical use. Full article
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18 pages, 4697 KiB  
Article
Audouin’s Gull Colony Itinerancy: Breeding Districts as Units for Monitoring and Conservation
by Massimo Sacchi, Barbara Amadesi, Adriano De Faveri, Gilles Faggio, Camilla Gotti, Arnaud Ledru, Sergio Nissardi, Bernard Recorbet, Marco Zenatello and Nicola Baccetti
Diversity 2025, 17(8), 526; https://doi.org/10.3390/d17080526 - 28 Jul 2025
Viewed by 376
Abstract
We investigated the spatial structure and colony itinerancy of Audouin’s gull (Ichthyaetus audouinii) adult breeders across multiple breeding sites in the central Mediterranean Sea during 25 years of fieldwork. Using cluster analysis of marked individuals from different years and sites, we [...] Read more.
We investigated the spatial structure and colony itinerancy of Audouin’s gull (Ichthyaetus audouinii) adult breeders across multiple breeding sites in the central Mediterranean Sea during 25 years of fieldwork. Using cluster analysis of marked individuals from different years and sites, we identified five spatial breeding units of increasing hierarchical scale—Breeding Sites, Colonies, Districts, Regions and Marine Sectors—which reflect biologically meaningful boundaries beyond simple geographic proximity. To determine the most appropriate scale for monitoring local populations, we applied multievent capture–recapture models and examined variation in survival and site fidelity across these units. Audouin’s gulls frequently change their location at the Breeding Site and Colony levels from one year to another, without apparent survival costs. In contrast, dispersal beyond Districts boundaries was found to be rare and associated with reduced survival rates, indicating that breeding Districts represent the most relevant biological unit for identifying local populations. The survival disadvantage observed in individuals leaving their District likely reflects increased extrinsic mortality in unfamiliar environments and the selective dispersal of lower-quality individuals. Within breeding Districts, birds may benefit from local knowledge and social information, supporting demographic stability and higher fitness. Our findings highlight the value of adopting a District-based framework for long-term monitoring and conservation of this endangered species. At this scale, demographic trends such as population growth or decline emerge more clearly than when assessed at the level of singular colonies. This approach can enhance our understanding of population dynamics in other mobile species and support more effective conservation strategies aligned with natural population structure. Full article
(This article belongs to the Special Issue Ecology, Diversity and Conservation of Seabirds—2nd Edition)
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17 pages, 1603 KiB  
Perspective
A Perspective on Quality Evaluation for AI-Generated Videos
by Zhichao Zhang, Wei Sun and Guangtao Zhai
Sensors 2025, 25(15), 4668; https://doi.org/10.3390/s25154668 - 28 Jul 2025
Viewed by 321
Abstract
Recent breakthroughs in AI-generated content (AIGC) have transformed video creation, empowering systems to translate text, images, or audio into visually compelling stories. Yet reliable evaluation of these machine-crafted videos remains elusive because quality is governed not only by spatial fidelity within individual frames [...] Read more.
Recent breakthroughs in AI-generated content (AIGC) have transformed video creation, empowering systems to translate text, images, or audio into visually compelling stories. Yet reliable evaluation of these machine-crafted videos remains elusive because quality is governed not only by spatial fidelity within individual frames but also by temporal coherence across frames and precise semantic alignment with the intended message. The foundational role of sensor technologies is critical, as they determine the physical plausibility of AIGC outputs. In this perspective, we argue that multimodal large language models (MLLMs) are poised to become the cornerstone of next-generation video quality assessment (VQA). By jointly encoding cues from multiple modalities such as vision, language, sound, and even depth, the MLLM can leverage its powerful language understanding capabilities to assess the quality of scene composition, motion dynamics, and narrative consistency, overcoming the fragmentation of hand-engineered metrics and the poor generalization ability of CNN-based methods. Furthermore, we provide a comprehensive analysis of current methodologies for assessing AIGC video quality, including the evolution of generation models, dataset design, quality dimensions, and evaluation frameworks. We argue that advances in sensor fusion enable MLLMs to combine low-level physical constraints with high-level semantic interpretations, further enhancing the accuracy of visual quality assessment. Full article
(This article belongs to the Special Issue Perspectives in Intelligent Sensors and Sensing Systems)
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17 pages, 1728 KiB  
Article
The Impact of Colony Deployment Timing on Tetragonula carbonaria Crop Fidelity and Resource Use in Macadamia Orchards
by Claire E. Allison, James C. Makinson, Robert N. Spooner-Hart and James M. Cook
Plants 2025, 14(15), 2313; https://doi.org/10.3390/plants14152313 - 26 Jul 2025
Viewed by 285
Abstract
Crop fidelity is a desirable trait for managed pollinators and is influenced by factors like competing forage sources and colony knowledge of the surrounding environment. In European honey bees (Apis mellifera L.), colonies deployed when the crop is flowering display the highest [...] Read more.
Crop fidelity is a desirable trait for managed pollinators and is influenced by factors like competing forage sources and colony knowledge of the surrounding environment. In European honey bees (Apis mellifera L.), colonies deployed when the crop is flowering display the highest fidelity. We tested for a similar outcome using a stingless bee species that is being increasingly used as a managed pollinator in Australian macadamia orchards. We observed Tetragonula carbonaria (Smith) colonies deployed in macadamia orchards at three time points: (1) before crop flowering (“permanent”), (2) early flowering (“early”), and (3) later in the flowering period (“later”). We captured returning pollen foragers weekly and estimated crop fidelity from the proportion of macadamia pollen they collected, using light microscopy. Pollen foraging activity was also assessed via weekly hive entrance filming. The early and later introduced colonies initially exhibited high fidelity, collecting more macadamia pollen than the permanent colonies. In most cases, the permanent colonies were already collecting diverse pollen species from the local environment and took longer to shift over to macadamia. Pollen diversity increased over time in all colonies, which was associated with an increase in the proportion of pollen foragers. Our results indicate that stingless bees can initially prioritize a mass-flowering crop, even when flowering levels are low, but that they subsequently reduce fidelity over time. Our findings will inform pollinator management strategies to help growers maximize returns from pollinator-dependent crops like macadamia. Full article
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20 pages, 3386 KiB  
Article
Design of Realistic and Artistically Expressive 3D Facial Models for Film AIGC: A Cross-Modal Framework Integrating Audience Perception Evaluation
by Yihuan Tian, Xinyang Li, Zuling Cheng, Yang Huang and Tao Yu
Sensors 2025, 25(15), 4646; https://doi.org/10.3390/s25154646 - 26 Jul 2025
Viewed by 386
Abstract
The rise of virtual production has created an urgent need for both efficient and high-fidelity 3D face generation schemes for cinema and immersive media, but existing methods are often limited by lighting–geometry coupling, multi-view dependency, and insufficient artistic quality. To address this, this [...] Read more.
The rise of virtual production has created an urgent need for both efficient and high-fidelity 3D face generation schemes for cinema and immersive media, but existing methods are often limited by lighting–geometry coupling, multi-view dependency, and insufficient artistic quality. To address this, this study proposes a cross-modal 3D face generation framework based on single-view semantic masks. It utilizes Swin Transformer for multi-level feature extraction and combines with NeRF for illumination decoupled rendering. We utilize physical rendering equations to explicitly separate surface reflectance from ambient lighting to achieve robust adaptation to complex lighting variations. In addition, to address geometric errors across illumination scenes, we construct geometric a priori constraint networks by mapping 2D facial features to 3D parameter space as regular terms with the help of semantic masks. On the CelebAMask-HQ dataset, this method achieves a leading score of SSIM = 0.892 (37.6% improvement from baseline) with FID = 40.6. The generated faces excel in symmetry and detail fidelity with realism and aesthetic scores of 8/10 and 7/10, respectively, in a perceptual evaluation with 1000 viewers. By combining physical-level illumination decoupling with semantic geometry a priori, this paper establishes a quantifiable feedback mechanism between objective metrics and human aesthetic evaluation, providing a new paradigm for aesthetic quality assessment of AI-generated content. Full article
(This article belongs to the Special Issue Convolutional Neural Network Technology for 3D Imaging and Sensing)
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47 pages, 18189 KiB  
Article
Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures
by Zineb Sordo, Eric Chagnon, Zixi Hu, Jeffrey J. Donatelli, Peter Andeer, Peter S. Nico, Trent Northen and Daniela Ushizima
J. Imaging 2025, 11(8), 252; https://doi.org/10.3390/jimaging11080252 - 26 Jul 2025
Viewed by 611
Abstract
Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks [...] Read more.
Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model’s foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research. Full article
(This article belongs to the Special Issue Celebrating the 10th Anniversary of the Journal of Imaging)
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