Existing meta-path-based recommendation systems suffer from noise interference and intent fusion, which distort high-order semantic relationships and consequently degrade recommendation performance. To tackle these challenges, we propose meta-path Diffusion Denoising and Multi-view Contrastive Learning for recommendation (DDMCL), which enhances representation learning from three perspectives: (1) Dual-graph diffusion denoising: we employ diffusion model to simultaneously reconstruct the latent semantic distributions of two meta-path graphs on the user (item) side, achieving joint optimization through a shared-parameter network with graph-specific identifiers to enhance denoising performance while preserving semantic consistency. (2) Intent attention fusion: we extract path semantic representations based on graph convolutional networks, design an attention mechanism to dynamically aggregate intent features, and use the embedding of the user-item graph as a structural guide to capture the multi-dimensional preferences of users and items. (3) Multi-view contrastive optimization: we perform intent-aware contrastive learning in the meta-path semantic space and structure-enhanced contrastive learning in the user-item interaction space, jointly optimized with the main recommendation objective to improve robustness and generalization. Extensive experiments on six benchmark recommendation datasets demonstrate that DDMCL outperforms state-of-the-art baselines, improving the NDCG@20 on the Amazon and Yelp datasets by 3.27% and 3.70%, respectively.
Rapid urbanization and unprecedented population mobility in China have intensified the need to understand the drivers of migrants’ settlement decisions. Despite the central role of daily mobility in shaping urban experiences, little is known about how transportation convenience perceptions and travel modes influence long-term settlement intentions. This study addresses this gap by developing a multi-dimensional behavioral‑perceptual framework of transportation convenience, moving beyond single‑dimension perceptual or amenity‑based approaches. Crucially, it further examines how the effects of these perceptions vary across travel modes (walking, biking, metro, bus, and car), an aspect largely overlooked in prior research. Using the 2022 City Health Examination Survey, covering 240,177 migrants across 57 Chinese cities, and employing multi-level mixed-effects models, we examine the associations between transportation perceptions, travel modes, and settlement intentions. Findings show that more positive perceptions of transportation convenience are positively associated with settlement intentions, including the walk environment (β = 0.124, P < 0.01), riding environment (β = 0.090, P < 0.01), public transport interchanges (β = 0.131, P < 0.01), road congestion (β = 0.097, P < 0.01), and commuting time (β = 0.089, P < 0.01). The influence of transportation perceptions varies across travel modes, with active travellers (walkers and cyclists) most sensitive, public transit users moderately sensitive, and car users least sensitive. This gradient reflects how daily mobility conditions intersect with economic capacity, lifestyle, and urban integration experiences. The study contributes to theory by operationalizing behavioral attitudes in a multi-dimensional mobility framework and provides actionable guidance for urban planners. Optimizing transport infrastructure, enhancing neighborhood amenities, and tailoring interventions to specific travel modes can strengthen migrants’ sense of belonging and promote inclusive, sustainable urban development.
Edible fungal mycelium has become a promising food ingredient and material due to its high protein conversion efficiency, nutritional benefits, and fibrous properties suitable for structural support. This study introduces an innovative method for fermenting mycelium within a grid to improve the nutritional and mechanical properties of 3D-printable ink. This fermentation process significantly increased the protein content to 66.20 +/- 1.62% (by 1.52 times compared to unfermented samples), refined amino acid profiles (EAA/NEAA = 51%), and boosted biological activity, especially antioxidant capabilities. Simultaneously, the mycelium acts as a natural binder within the grid, enhancing structural stability, mechanical strength, and texture. Experimental data showed a significant reduction in shrinkage (37.46% horizontally and 30.08% vertically) and a notable improvement in WHC (15.64% and 13.99%). Additionally, mechanical strength and hardness rose by 352.96% and 589.49%, respectively. This composite ink contains mycelium, soy protein, and polysaccharides, sourced from two edible fungal strains (Pleurotus ostreatus and Ganoderma lucidum). By inducing structural anisotropy and integrating fermentation techniques, the mycelium develops a fibrous texture approximating that of muscle tissue, thereby elevating both nutritional value and sensory complexity. Notably, this approach addresses traditional limitations of 3D-printable materials, such as structural instability and insufficient protein content, offering a sustainable and high-performance alternative for plant-based meat substitutes.
The interpretability of models is crucial in high-stakes fields such as healthcare, as it allows researchers and physicians to understand the rationale behind decisions, thereby enhancing diagnostic credibility and reliability. Although prototype learning methods have shown great potential in providing interpretability, designing a network that achieves both high classification accuracy and medical interpretability in diagnostic tasks remains a significant challenge. Particularly when dealing with medical images where inter-class differences are subtle and complex, it is essential to extract rich, multi-scale features and obtain prototypes that intuitively reflect key lesion characteristics.To address these challenges, this paper proposes PRototype-Oriented Visual Explanation Network (PROVEN), a self-explanatory medical image classification method. This method effectively alleviates the issues of prototype redundancy and insufficient representativeness in traditional prototype learning by integrating two complementary types of prototypes: “Support Prototypes”, which focus on learning class boundaries to handle ambiguous samples, and “Trivial Prototypes”, which capture core class features to facilitate efficient classification. Furthermore, the model introduces a novel loss function—the Shallow-Deep Feature Consistency Loss (SDFCL)—to enhance the interaction between shallow and deep features, ensuring that local details are effectively propagated into deep representations.Extensive experiments were conducted on four public medical image datasets, including two cervical cytology datasets (SIPaKMeD and Hussian LBC) and two skin lesion datasets (PH2D7 and HAM10000), containing more than 16,000 images in total. Following the standard evaluation protocols of these benchmarks, PROVEN consistently outperforms state-of-the-art CNN-, Transformer-, and prototype-based methods. Compared with the best-performing prototype-based approaches, PROVEN achieves accuracy improvements of 5.80% on PH2D7 and 7.73% on HAM10000. These results demonstrate that PROVEN provides stronger inherent interpretability while achieving superior classification performance.
As a resource-oriented approach to domestic sewage management, the recycling of source-separated urine for agricultural use has garnered significant attention. To mitigate the risks posed by pathogens and elevated ammonia concentrations associated with urine collection and agricultural application, this study proposes and investigates an eco-friendly approach using Agricultural Jiaosu (AJ) and molasses for urine fermentation. Experimental results demonstrated that fermentation dramatically reduced free ammonia concentration and significantly inhibited pathogens in the urine from day 5 onwards. After 10-day fermentation period, the free ammonia was reduced to around 4.8% of the initial value in pure urine, and pathogens in the fermentation broth were reduced to below 7% in relative abundance for the large-scale experiment. The absolute abundances of Lactic Acid Bacteria (LAB) and Pichia spp. yeast both increased by more than an order of magnitude by day 10, with a strong positive correlation between them. These results indicate that the fermented solution is a safer urine-AJ combined blend, which can be beneficial to both crops and soil when diluted properly. To be economical and efficient, a 3% molasses addition and a 10-day fermentation period were recommended. This study provides an eco-friendly method for co-treating kitchen waste and source-separated urine, with potential applications in decentralized rural sanitation and agricultural biofertilizer production.
