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Background/Objectives: The increasing burden of non-communicable diseases, together with accelerating environmental degradation, highlights the urgent need for sustainable dietary patterns that promote both human and planetary health. The Mediterranean diet (MedDiet), traditionally followed in countries bordering the Mediterranean basin, has gained recognition as a model of sustainable nutrition due to its well-documented health benefits and relatively low environmental impact. However, its broader role within sustainable food systems requires comprehensive and interdisciplinary evaluation. The aim of this review is to provide a state-of-the-art synthesis of the evidence on the MedDiet as a sustainable dietary pattern, integrating its health, environmental, economic, and socio-cultural dimensions. Methods: This state-of-the-art narrative review synthesizes evidence from peer-reviewed literature on the MedDiet and sustainability. Relevant studies were identified through major scientific databases, focusing on publications addressing nutritional, environmental, economic, and socio-cultural dimensions. Both observational and interventional studies, as well as modeling and life cycle assessment analyses, were included. Additional sources from international organizations and policy reports were incorporated to contextualize global trends and challenges. Results: High adherence to the MedDiet is consistently associated with a reduced risk of cardiovascular disease, type 2 diabetes, cancer, and all-cause mortality. From an environmental perspective, the MedDiet is associated with lower greenhouse gas emissions, reduced land and water use, and enhanced biodiversity conservation compared with Western dietary patterns. Economically, it may represent a cost-effective dietary model and support local food systems when grounded in traditional practices, although affordability varies across contexts. Socio-culturally, the MedDiet promotes food heritage, culinary skills, and social cohesion. Nevertheless, globalization, urbanization, and the increasing consumption of ultra-processed foods have contributed to declining adherence, posing significant challenges to its sustainability and scalability. Moreover, the sustainability benefits of the MedDiet seem to be context-dependent rather than intrinsic, raising several challenges and limitations for its adoption. Conclusions: The MedDiet should be viewed not as a definitive solution to global food-system challenges but as a valuable reference model that illustrates how dietary practices can contribute simultaneously to human health, environmental sustainability, and cultural continuity. Modern sustainable dietary strategies should build upon the strengths of the MedDiet while recognizing its limitations, embracing contextual adaptation, and addressing the structural determinants that shape food choices. Full article

From the perspective of process, time may be viewed as that which marks the occurrence of change, as previously proposed by this author. In contrast, spatial distinctions may be viewed as enabling the individuation and counting of events generated by processes. Following a conceptual discussion of Whitehead’s process theory, temporal distinctions, and spatial distinctions, a formal model of spacetime as history is presented based upon process actionsas generators of spacetime, and a new geometric concept of `thereness’ is introduced. Each process action propagates information to the next generation (time) and to a particular `there’ (space). This generates a mixed multigraph where the directed subgraph represents the timelike component (causal propagation of information) and the undirected subgraph represents the spacelike component (informational correlations arising from common causes). A spatial position is an equivalence class of generated events; thus, it is emergent. Each spacetime is local to its generating process, consistent with the concept of local becoming proposed by Arthur. If the set of process actions forms a commutative monoid, then the resulting spacetime takes the form of a discrete lattice. It is speculated that the intransitivity and incompleteness of the spacelike subgraph may be linked to the presence of contextuality. Full article

Large language models (LLMs) are increasingly being used in radiology-related workflows, but their application to reference, regulatory, and methodological queries remains limited by hallucinations and the static nature of model knowledge. This study aimed to develop and evaluate a retrieval-augmented generation (RAG) system for radiologists designed to provide grounded responses to such queries. A knowledge base was created through a survey of practicing radiologists and expert validation of sources, resulting in a corpus of 1049 documents. The system incorporated structured document parsing, a two-level parent–child vector database, hybrid dense–sparse retrieval, reranking, and a local large language model. Performance was assessed through functional testing, automated LLM-as-a-judge metrics, and multireader expert evaluation by 16 radiologists using 400 technical queries. No hallucinations were detected in the 77-query functional testing set during expert review. On the full technical dataset, automated Contextual Precision, Contextual Recall, and Answer Relevancy were 0.735, 0.881, and 0.890, respectively. Expert evaluation showed high response accuracy (mean, 4.53/5) and high expert-assessed Contextual Precision (0.886). Inter-expert agreement was substantial to excellent for most Likert-scale criteria. These findings suggest that a hierarchical RAG architecture can provide reliable access to radiology-specific reference information, although external validation and automated updating of the knowledge base remain necessary. Full article

Text detection models based on DBNet have demonstrated strong performance in natural scene text detection. However, these models still suffer from the following three issues. Firstly, the amplifying factor hyperparameter in the differentiable binarization (DB) makes it difficult for the text detection model to achieve optimal performance. Secondly, the integration of low-level and high-level features within the backbone’s feature pyramid lacks specific optimization strategies. Thirdly, the deconvolution operation in the prediction head may damage text contours. To tackle the aforementioned issues, this paper presents a text detection model termed ASM-DBNet, which mainly consists of three innovations. For the first issue, an adaptive differentiable binarization (ADB) scheme is proposed. It can independently predict amplifying factor for feature points at different spatial locations and replace the original amplifying factor hyperparameter, thereby improving the overall optimization performance of the model. For the second issue, a spatial-channel self-attention (SCA) module is proposed to optimize the fusion of high-level and low-level features. On the one hand, spatial self-attention is used to enhance the spatial localization ability of high-level features; on the other hand, channel self-attention based on a grouped transformer is used to optimize the fusion results of high-level and low-level features. For the third issue, a multi-scale context-enhanced dynamic upsampling (MC-DyUpS) module is proposed to replace the deconvolution operation in the prediction head. It enhances contextual perception in the region of interpolation points through multi-scale context feature extraction, and then accurately predicts coordinate offsets of interpolation points. The position correction based on these offsets effectively suppresses the spatial deviation caused by deconvolution. Ablation studies demonstrate the effectiveness of the SCA module, MC-DyUpS module, ADB scheme, and their arbitrary combinations. Comprehensive quantitative evaluations demonstrate that ASM-DBNet achieves competitive F1-scores of 84.1%, 84.2%, and 85.7% on the ICDAR 2015, Total-Text, and MSRA-TD500 datasets, respectively, with improvements of 1.8%, 1.4%, and 2.9% over the baseline model. Full article

Climate-related disasters increasingly threaten agricultural sustainability and agro-ecological systems, yet public engagement with these risks often remains limited because climate impacts are perceived as psychologically distant. This study examined whether AI-generated audiovisual simulations of climate-related disasters are associated with stronger emotional and action-oriented engagement responses, particularly when scenarios are presented in a familiar local context. Using an experimental survey design, 402 participants broadly reflecting the characteristics in Israel viewed four short AI-generated films depicting wildfire and tsunami scenarios in either local (Israel) or geographically distant settings. Participants were explicitly informed that the videos were generated using artificial intelligence tools. After viewing, participants ranked the scenarios according to emotional response, concern about future implications, perceived personal relevance, and willingness to take action. The findings show a consistent pattern in which locally framed scenarios elicited stronger responses across all four dimensions than geographically distant scenarios. Wildfire scenarios set in Israel were rated as the most emotionally impactful, personally relevant, and action-motivating. Additional differences were observed across demographic groups, with higher engagement among women, younger participants, and respondents with higher educational attainment. These results suggest that AI-generated simulations, especially when locally contextualized, may serve as a potentially useful communication tool for reducing psychological distance and strengthening public engagement with climate-related environmental risks that may indirectly affect agricultural sustainability and agro-ecological resilience. Full article

Single-index greening trends can misrepresent post-mining recovery because they do not show whether disturbed surfaces are converging toward the spectral conditions of nearby stable vegetation. Here, we present a 22-year (2003–2024) Landsat-based assessment of the Nannihu molybdenum mine (Henan, China) by combining LandTrendr-based disturbance and recovery timing from annual NBR series with a benchmark-relative spectral recovery index (RSRI) and five-epoch random forest land-surface classification used as contextual support. The classifier was trained on 2024 samples and transferred to earlier epochs without independent validation at each epoch. Historical class labels should therefore be treated as approximate contextual support. A five-type recovery pathway typology showed that only 41.8% of mine-affected pixels followed vegetated recovery pathways, while 28.2% stabilized as non-vegetated surfaces and 25.0% remained under persistent disturbance. Even the combined vegetation recovery type had a mean RSRI of only 0.309 (SD = 0.143), suggesting that greening alone does not imply close benchmark-relative spectral proximity to the local stable-vegetation reference. Disturbance magnitude was the feature most strongly associated with RSRI variation (XGBoost SHAP mean, |SHAP| = 0.075). The RSRI quantifies benchmark-relative spectral proximity using local stable-vegetation benchmarks, and it does not measure species composition, biomass, or ecosystem function. This site-specific case study indicates that benchmark-relative spectral assessment can complement conventional greening metrics in retrospective mine monitoring using open-access Landsat archives, with field validation the natural next step toward linking these spectral findings to ecological or functional recovery. Full article

Mirror segmentation remains challenging because mirror regions often share appearance with the reflected scene, while sensor depth around mirrors is frequently missing, noisy, or geometrically inconsistent. Although recent RGB-based methods have achieved strong results by exploiting contextual and symmetry-aware cues, their ability to use geometric information reliably is still limited. In this paper, we propose a reliable RGB-D mirror segmentation framework built upon SATNet. Specifically, we extend the symmetry-aware baseline with a dedicated depth branch that injects hierarchical sensor-depth features into the multi-scale decoder, and we introduce a Reliability-Guided Residual Correction Module (RGRCM) for final prediction refinement. Instead of treating predicted depth as an independent modality branch, RGRCM internally constructs dual-depth evidence from sensor depth and monocular depth estimated by a pretrained Depth Anything v2 model, encoding raw depth observations, cross-depth discrepancies, validity cues, and local depth instability. The resulting evidence is used to guide uncertainty-aware residual correction only in regions where depth-driven refinement is likely to be beneficial. Experiments on the RGBD-Mirror benchmark show that the proposed method achieves 83.57 IoU, 0.899 $F_{\beta }$, 0.026 MAE, and 6.26 BER, outperforming existing RGB and RGB-D mirror segmentation methods. Full article

Natural disasters frequently cause significant building damage, necessitating timely and accurate damage assessment for effective rescue operations and post-disaster reconstruction. Traditional building damage assessment methods commonly rely on paired pre- and post-disaster remote sensing images, which often face practical challenges in data acquisition and image pairing during emergency situations. To overcome these limitations, a hybrid swin–mamba U-shaped network (UNet) is developed for building damage assessment using only post-disaster remote sensing imagery. The proposed framework employs a Swin Transformer as the encoder to extract multi-scale features and capture long-range contextual information, while a Parallelized Patch-Aware Attention (PPA) convolution module is introduced in the decoder to restore spatial details and improve feature reconstruction. In addition, a Visual State Space (VSS) module is incorporated in the bottleneck layer to effectively model both global contextual dependencies and local structural information, thereby improving the representation of building damage characteristics from single-temporal imagery. Experiments conducted on the xBD dataset show that the proposed method outperforms the Swin–Unet by 1.7% in overall F1-score, achieving an overall F1-score of 55.2%. In addition, qualitative visualization results suggest that the proposed method has favorable generalization capability across different disaster scenarios. These results highlight the practical potential of the proposed framework for rapid post-disaster building damage assessment, particularly in emergency response scenarios where only post-disaster imagery is available. Full article

Personalized reconstruction of missing-tooth morphology is a key problem in digital prosthodontics. The main challenge is to generate results that are consistent with the patient’s local dentition, the morphology of the contralateral teeth, and anatomically plausible occlusal details. Although several deep learning-based methods have been proposed for dental restoration, existing approaches still have limitations, including insufficient use of patient-specific contextual information, oversmoothed boundary structures in the generated results, and relatively high model complexity. To address these limitations, this study proposes a CBAM-Sobel conditional Pix2Pix framework, termed CS-cPix2Pix, for predicting the height map of a missing tooth from height projection maps of the contralateral teeth and adjacent teeth. The framework uses height projection maps of a three-tooth contralateral region and an adjacent-tooth region as conditional inputs. A U-Net generator is adopted to learn the mapping from the input conditions to the target missing-tooth height map, and a convolutional block attention module is introduced in the encoder to enhance feature representation in key morphological regions. Furthermore, a Sobel edge loss is incorporated in addition to the adversarial loss and L1 reconstruction loss to constrain the local gradient structure of the generated height map and reduce oversmoothing of occlusal edges, grooves, and ridges. Experimental results show that CS-cPix2Pix achieves better overall quantitative performance than the baseline Pix2Pix model and multiple ablation models, especially in terms of PSNR, FSIM, IoU, and Sobel-L1. Under the current experimental setting, the proposed method generates missing-tooth height maps with clearer boundaries and more continuous structures, and it supports relatively stable reconstruction of three-dimensional occlusal surface meshes from the predicted height maps. However, the present model development still mainly relies on a single public orthodontic dental dataset and focuses primarily on teeth numbered 4, 5, and 6. Therefore, the generalization of the proposed method to other tooth positions, other scanners, different populations, and different acquisition conditions still requires further verification. Full article

Artificial intelligence (AI) is becoming increasingly embedded in the digital infrastructure of local government, creating new opportunities to improve public sector productivity while also influencing systemic risk and organisational resilience across interconnected public systems. As municipalities adopt AI to automate, support, and transform administrative processes, organisational performance becomes more dependent on the reliability of algorithms, the quality of data, effective governance, and coordination among public institutions. These growing interconnections create new vulnerabilities that can spread across public service networks, yet evidence on the productivity, risk, and resilience implications of AI adoption remains fragmented and dispersed across different fields of research. This study develops an integrative conceptual framework that examines the relationship between AI adoption, public sector productivity, systemic risk, and organisational resilience within interconnected sociotechnical systems. Drawing on insights from productivity economics, systems theory, and public governance, the framework positions total factor productivity (TFP) within a broader public value and risk governance perspective. Using the PRISMA 2020 methodology, the study systematically reviews 68 peer reviewed empirical studies published between 2015 and 2025, assessing productivity outcomes, methodological quality, effect sizes, and contextual factors relevant to local government and networked public administration. The findings show that productivity gains associated with AI are strongly influenced by organisational readiness, including digital maturity, workforce capabilities, governance quality, and institutional coordination. While AI has the potential to improve operational efficiency and strengthen adaptive capacity, inadequate readiness can increase systemic risks arising from algorithmic opacity, cybersecurity challenges, data dependence, coordination failures, and disruptions that may spread across interconnected administrative systems. The review also highlights that resilience depends on the ability of public organisations to anticipate, absorb, adapt to, and recover from AI-related disruptions while maintaining the continuity and quality of public services. The study contributes to theory by integrating perspectives from productivity economics, public administration, and systemic risk within a sociotechnical systems framework. It contributes empirically through a comprehensive synthesis of evidence on AI and public sector productivity and methodologically through the application of transparent PRISMA 2020 review procedures. From a practical perspective, the study offers a conceptual measurement framework and policy guidance for municipal decision makers seeking to improve productivity while strengthening resilience and reducing systemic risks in increasingly interconnected public governance systems. Full article

Blind image watermarking models such as HiDDeN have laid an important foundation for end-to-end watermarking. Nevertheless, they still suffer from three major limitations: single-scale feature extraction, fixed fusion weights, and slow training convergence. To address these issues, this paper proposes an adaptive multi-scale watermarking algorithm based on collaborative dual-attention mechanisms. The algorithm designs an adaptive multi-scale feature fusion module (MA-FFM) with a dynamic gating network in the encoder, which flexibly combines local multi-scale textures with global contextual information, overcoming the limitation of fixed fusion weights. In the decoder, a multi-level channel attention module is embedded to strengthen the extraction of watermark signals. The two attention modules work synergistically: the encoder focuses on adaptive feature fusion while the decoder leverages channel attention to selectively enhance watermark-related features, forming a dual-attention synergy that balances robustness and imperceptibility. Moreover, the dynamic gating network adaptively adjusts the contribution of local versus global features via learnable weights, whose evolution from approximately 0.51 to about 0.89 improves model interpretability. Experiments are conducted on the COCO 2017 dataset. Compared with HiDDeN, the proposed algorithm reduces the bit error rate (BER) from 0.1696 to 0.1538 under no attack with a relative reduction of 9.3%, increases PSNR by 0.61 dB, and improves SSIM from 0.9058 to 0.9077. Under various attacks—including JPEG compression, Gaussian noise, salt-and-pepper noise, and brightness/contrast adjustments—the BER remains consistently lower than that of HiDDeN. Ablation studies confirm the effectiveness of each module. Overall, the proposed algorithm preserves visual quality, improves the accuracy of watermark embedding and extraction, and exhibits strong generalization robustness against common image distortions. Full article

GLP-1 receptor agonists (GLP-1RAs) are widely used in the treatment of type 2 diabetes and obesity and are increasingly relevant in periodontal and implant practice. This review covers mechanisms, preclinical and early human evidence, and practical periodontal considerations; the structured database search is conducted in accordance with the Scale for the Assessment of Narrative Review Articles (SANRA) and the International Committee of Medical Journal Editors (ICMJE) principles. Two pathways explain GLP-1RAs’ relevance: indirect effects from better glycemic control, weight loss, and reduced inflammation; and direct tissue effects involving GLP-1R signaling and the GLP-1/dipeptidyl peptidase-4 (DPP-4) axis. Preclinical studies show reduced inflammation, osteoclast activity, and alveolar bone loss, along with improved periodontal stem cell function under hyperglycemia or inflammation via Nuclear Factor-kappaB (NF-kappaB), Wingless-related integration site (Wnt)/beta-catenin, and Mitogen-Activated Protein Kinase (MAPK) pathways. Animal studies on implants and local delivery, including exendin-4 platforms, suggest osteometabolic benefits. Human data are limited and mostly observational, and confounders include metabolic status, smoking, medication, and nutrition. Oral side effects such as xerostomia and dehydration are also noted. At present, GLP-1RA therapy should be regarded as a contextual modifier of periodontal risk and healing capacity rather than as a stand-alone periodontal therapy. Full article

Across semi-arid and environmentally vulnerable regions, intensifying climate pressures, land degradation, and resource scarcity are placing growing demands on institutions, communities, and land users. However, the knowledge and technical skills required to respond effectively remain uneven and often poorly aligned with local needs. This study presents a comparative skills audit in Kimberley, Upington, and Rietfontein in the Northern Cape, identifying capacity gaps, stakeholder-specific training priorities, and structural barriers in natural resource and sustainable land management. Using questionnaires, semi-structured interviews, participatory site visits, and multi-stakeholder consultations, competencies were assessed across GIS and remote sensing, climate resilience, soil and land restoration, water conservation, sustainable agriculture, and policy literacy. Results show significant disparities in skills proficiency. GIS and remote sensing (0.8) and climate resilience strategies (1.0) were weakest, while policy literacy (1.5) and soil management (2.0) were also limited. Sustainable agriculture (4.0) and water conservation (2.8) showed relatively stronger capacity. Training needs varied by stakeholder, with government prioritizing geospatial tools and governance, and farmers emphasizing climate adaptation and resource management. Key barriers include limited digital infrastructure (83%), insufficient government support (80%), high training costs (78%), and contextual mismatches (50%). Integrated, place-based capacity development is essential to strengthen adaptive governance and long-term resilience. Full article

Breast density has a significant impact on how clearly masses appear in mammography. It can also introduce bias in automatic localization systems when density distributions are uneven. Although advances in deep learning-based detection methods have been made, most studies report overall performance without explicitly accounting for variability associated with breast density. Breast cancer diagnosis from mammography is strongly influenced by dataset composition, annotation variability, and breast density distribution, factors that are rarely controlled in current AI evaluations. We introduce Mass-Bench, a clinically balanced and harmonized multi-dataset benchmark that integrates CBIS-DDSM, INBREAST, VINDr-Mammo, and DMID under a unified canonical schema, with standardized ACR density and BI-RADS encoding. Using a leakage-controlled and distribution-aware evaluation protocol, density-stratified mass detection and lesion-centered regions of interest (ROIs) classification were assessed across datasets. YOLO-based detection models achieved peak area under the curve (AUC) values up to 0.943; however, performance systematically degraded with increasing ACR density, revealing limitations that are often masked in imbalanced evaluations. By enforcing clinically representative density distributions, Mass-Bench provides a more reliable estimation of localization performance, which directly impacts downstream clinical tasks. In this context, binary ACR classification achieved F1-scores up to 0.976, while binary BI-RADS discrimination reached accuracies up to 0.93. However, multi-class classification remained more challenging, showing increased sensitivity to dataset heterogeneity and contextual information. These findings demonstrate that conventional evaluations may overestimate robustness, particularly in dense breast categories, and highlight the importance of density-aware benchmarking for developing reliable and clinically applicable AI systems in mammography. Full article

With the widespread use of social media and online platforms in the dissemination of public opinion within universities, the multi-class classification of risk-related texts has become a critical component of online public opinion analysis in higher education institutions. Existing multi-class risk classification methods often focus on static semantic representations, making it difficult to effectively capture the emotional evolution within texts and the differences between samples, which in turn affects the accuracy of risk classification. To address this, this paper proposes a multi-class risk classification model for university online public opinion that integrates contextual semantic modeling, emotional evolution detection, and adaptive confidence-based feature fusion. The model employs pre-trained BERT for context encoding and, while preserving high-level semantic information, enhances the model’s adaptability to domain-specific features through a selective unfreezing strategy. First, a Bidirectional Gated Recurrent Unit (BiGRU) is introduced to model the emotional evolution trajectory within text sequences, and an emotional transition intensity metric is constructed by calculating the difference between adjacent hidden states, thereby explicitly capturing the magnitude of emotional changes. Additionally, a convolutional feature branch is designed to capture local emotional patterns, enhancing the model’s ability to perceive local risk cues and fine-grained emotional fluctuations. Finally, the Emotion-Adaptive Feature Mixer (EAFM) is introduced. This module adaptively weights global emotional evolution features and local emotional pattern features based on sample confidence to adjust the contributions of different feature branches in risk classification. Experimental results demonstrate that the proposed model exhibits good convergence characteristics in the university online public opinion scenario represented by the CUOPO dataset and demonstrates strong interpretability through attention visualization and confidence coefficient analysis. Full article