Search Results (6,065)

In the 21st century, rapid urban development during global urbanization has led to high-density environments. These settings have become a significant cause of stress-related health problems for residents. Healing street design plays an important role in helping address mental health challenges caused by this process. Current research often focuses on healing elements and methods from only a single field. As a result, it lacks the integration of multidisciplinary and multi-stakeholder perspectives. To address this gap, this paper formed a Delphi expert panel with multidisciplinary scholars, urban managers, and practicing designers. The panel developed a quantitative evaluation model. This model covers four core dimensions: Safety (0.3210), Attractiveness (0.1080), Friendliness (0.2155), and Comfort (0.3553). It also includes eleven healing elements, such as Pedestrian Right-of-Way (0.4131), Night Lighting (0.3209), Visual Landscape (0.759), Street Furniture (0.4000), and Street Scale (0.3274). Using this model, the healing potential of Jingliu Road in Zhengzhou was assessed. The analysis identified the overall healing potential, core healing dimensions, and shortcomings of the street. This finding provides a clear direction for future healing-oriented street design. This paper builds a healing system for pedestrian spaces in high-density urban streets in China. It thus offers an evidence-based scientific tool for environmental design. Healing environments have expanded from less accessible spaces, such as squares and parks, to interactive and accessible streets. This transition enhances urban spaces’ capacity to address residents’ mental health concerns and promotes public health. Additionally, this paper offers specific recommendations for planners and policymakers to prioritize healing elements in urban renewal projects. Full article

Unsupervised change detection (CD) in heterogeneous remote sensing images is intrinsically difficult due to severe sensor-specific discrepancies. In the absence of ground truth, these discrepancies result in ambiguous optimization objectives that make it difficult for models to distinguish true land-cover changes from modality-driven pseudo-changes. To address these challenges, we propose MaskUCD, a novel unsupervised framework that reformulates heterogeneous CD as a dynamic mask-driven constraint scheduling problem. Fundamentally distinct from conventional strategies that enforce selective feature alignment, MaskUCD employs a spatially adaptive optimization mechanism. Specifically, the iteratively refined mask serves as a geometric reference to guide optimization. It enforces strict feature alignment in mask-unchanged regions to suppress modality-induced discrepancies, while simultaneously promoting feature divergence in mask-changed regions to emphasize semantic inconsistencies. In this way, explicit optimization objectives are established, together with an intrinsic interpretability constraint that guides the CD process. This strategy treats the mask as a structural guide for representation learning rather than a ground-truth reference, thereby avoiding error accumulation caused by directly using inaccurate masks as supervisory signals. To facilitate this optimization, we design a specialized asymmetric autoencoder with a hybrid encoder architecture, utilizing multi-scale frequency analysis and global context modeling to enhance feature representation capabilities. Consequently, this design enables the generation of refined and semantically consistent masks, which provide increasingly precise structural guidance, yielding converged and discriminative difference maps. Extensive experiments demonstrate that MaskUCD achieves state-of-the-art performance and superior robustness compared to existing advanced methods. Full article

Plant growth-promoting microorganisms (PGPMs) and microbial biocontrol agents have emerged as key tools for improving crop productivity while maintaining environmental sustainability. However, central questions remain regarding which factors determine their consistent field performance and how these factors interact under real agronomic conditions. Previous research has demonstrated that PGPMs enhance nutrient acquisition, regulate phytohormone balance, improve stress tolerance, and suppress plant pathogens through diverse biochemical and ecological mechanisms. Advances in omics technologies, genome mining, and synthetic microbial communities have further expanded understanding of their functional potential. Nevertheless, many studies rely on laboratory-scale experiments or short-term trials, with limited multi-season and cross-regional validation. This gap contributes to inconsistent field outcomes and restricts large-scale agricultural adoption. Long-term multi-season validation and reproducibility assessment remain essential priorities for improving reliability of microbial agricultural products. This review synthesizes recent advances in PGPM-based biofertilizers and microbial biocontrol technologies, critically examining their mechanisms of action, scalability constraints, formulation challenges, and regulatory limitations. It identifies major translational barriers, including context dependency, mechanistic uncertainties, reproducibility gaps, and insufficient systems-level integration. Full article

Neurodegenerative diseases (NDs) are increasingly considered neurometabolic disorders driven by early mitochondrial dysfunction, neuroinflammation, and synaptic alterations that precede clinical symptoms. This review summarises pre-clinical and experimental evidence suggesting that intermittent fasting (IF) may influence these early pathogenic processes by promoting metabolic switching, enhancing autophagy and mitochondrial quality control, and modulating neuroimmune pathways. We discuss recent advances in biomarker research supporting the early detection of neurodegenerative changes, including ultrasensitive analytical platforms that can identify neuronal, glial, and synaptic injury during preclinical stages. By integrating these biomarker developments with findings from human and experimental intermittent fasting studies, we highlight how high-sensitivity assays provide quantifiable insights into the neurometabolic effects of fasting. Furthermore, we discuss how precision nutrition strategies incorporating multimarker panels, phenotypic and epigenetic signatures, and longitudinal multi-omics profiling may facilitate personalised intermittent fasting protocols and improve monitoring of biological responses. Overall, these findings underscore the relevance of a clinical biochemistry perspective integrating advanced biomarker technologies to evaluate the neurometabolic effects of intermittent fasting as a potential early neuroprotective strategy for individuals at risk of neurodegeneration. Full article

Solute carrier family 25 member 39 (SLC25A39) is a pivotal mitochondrial glutathione transporter and an emerging oncoprotein in hepatocellular carcinoma (HCC). While its cell-intrinsic roles are increasingly recognized, its comprehensive functions in modulating the tumor immune microenvironment (TIME) and epigenetic landscape within HCC remain undefined. To address this, we employed an integrated multi-omics and experimental approach, including TCGA, ssGSEA, CCK-8, Transwell, etc. Our study confirmed SLC25A39 upregulation and its pro-tumorigenic role. Notably, we provide several key novel insights: First, we establish the first link between SLC25A39 promoter hypermethylation at specific CpG sites and poor patient prognosis, revealing an epigenetic regulatory layer in HCC. Second and most importantly, we pioneer the exploration of SLC25A39 in the HCC immune context, demonstrating its association with a distinct immunosuppressive TIME characterized by a Th2-skewed profile, reduced cytotoxic cell infiltration, and elevated immune checkpoint (CTLA-4, PD-1) expression. Furthermore, drug sensitivity analysis linked SLC25A39 to a broader spectrum of pharmacological agents beyond sorafenib. Collectively, our findings not only reinforce SLC25A39 as a therapeutic target but, for the first time, reposition it as a potential modulator at the intersection of tumor metabolism, epigenetics, and immunology in HCC, offering a rationale for its inhibition, particularly combined with immunotherapy. Full article

Triple-negative breast cancer (TNBC) represents a clinically challenging breast cancer (BC) subtype, characterized by aggressive behavior, high recurrence risk, and limited therapeutic options. MEAK7 has been identified as an alternative mTORC1 signaling pathway regulator; however, its role in BC and TNBC remains uninvestigated. This study aims to assess MEAK7 expression, prognostic significance, and therapeutic potential. We employed public datasets, including TCGA, bc-GenExMiner v5.2, GEPIA3, DOSurvive platforms, Kaplan–Meier Plotter, UALCAN, TIMER2.0, STRING, ENCORI, HPA, miRDB, TargetScan, and CRISPRdb. MEAK7 expression was significantly elevated in BC tissues versus normal breast tissue. MEAK7 expression was pronounced in TNBC and basal-like subtypes, with hypomethylation of its promoter region in TNBC. Elevated MEAK7 expression correlated with reduced disease-free survival (DFS) in TNBC and basal-like. Multivariate Cox regression identified MEAK7 as a significant prognostic factor for overall survival, independent of age and tumor stage. MEAK7 showed CRISPR-targetable gRNA profiles with high on-target efficiency and minimal off-target effects. Analyses revealed negative correlation with tumor-suppressive RNAs (miR-149-3p, miR-135a-5p, and LINC00993) and positive correlation with aggressive regulators (miR-135b-5p and HIF1A-AS2). This study represents one of the initial comprehensive and multi-platform bioinformatic analyses demonstrating that MEAK7 exhibits elevated expression in breast cancer, particularly within the aggressive TNBC. The findings indicate that MEAK7 may serve as a promising prognostic biomarker in TNBC biology and suggest its viability as a molecular candidate for future investigation in targeted therapeutic strategies. Full article

This review classifies plant-derived functional ingredients in pet food according to phytochemical groups and application forms, including direct oral supplementation and incorporation into complete diets. Polyphenols and plant extracts exert prominent antioxidant (singular), anti-inflammatory, immunomodulatory, and microbiome-regulating effects. Microalgae and omega-3 sources support lipid metabolism, cardiovascular function, and skin integrity. Cannabinoids demonstrate dose-dependent responses in dogs, while cats generally tolerate long-term administration and exhibit notable benefits in chronic pain management. Combinations of botanical extracts with complementary bioactives and fermented botanical preparations exhibit multi-target functionality, with dogs showing pronounced biochemical and microbiome modulation, whereas cats display more behavioral and functional improvements. Phytochemicals operate through integrated multi-level regulation, including activation of antioxidant enzymes, modulation of inflammatory cytokines and T-lymphocyte ratios, microbial metabolic shifts toward short-chain fatty acid production, and regulation of lipid metabolism. Dogs demonstrate marked effects on hepatic function, reproductive resilience, microbiome diversity, CD4+/CD8+ balance, and cholesterol control. In contrast, cats show greater benefits in inflammation reduction, pain relief, intestinal integrity, and long-term safety. These species-specific responses underscore the importance of precision formulation and highlight the emergence of plant-based “pharma-pet nutrition” integrating nutritional and biochemical strategies for targeted health promotion. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

Resource-based cities face unique land use challenges due to resource dependence and path lock-in, yet the driving mechanisms and future trajectories of their land use transitions remain underexplored. This study examines the Huaihai Economic Zone (HEZ), a representative coal-rich region in eastern China, to analyze land use changes from 2000 to 2023 and simulate 2036 scenarios under different development pathways. Using land use transfer matrices, dynamic degree metrics, and the Patch-generating Land Use Simulation (PLUS) model, we systematically identified spatiotemporal evolution patterns, quantified the contributions of driving factors, and projected multi-scenario future land use patterns. Results reveal that land use change in the study area was dominated by the conversion of cultivated land to construction land, alongside spatial restructuring from a monocentric to a polycentric network pattern. Notably, construction land expansion was least evident in the central Mining-Affected Zone, where land use changes remained relatively sluggish compared to other sub-regions. Driving factor analysis indicates that socio-economic factors primarily influenced changes in construction and cultivated land, while natural factors strongly affected ecological land and unused land. Multi-scenario simulations for 2036 demonstrate diverging trajectories: an urban development scenario would accelerate cultivated land loss and unused land expansion; a natural development scenario would maintain current pressures; and an ecological protection scenario would effectively curb urban sprawl while actively promoting ecological land recovery. This study concludes that transcending simple land use control to actively orchestrate “mining-urban-rural-ecological” spatial synergy is critical for achieving a sustainable transition in resource-based regions facing similar transformation pressures. Full article

To enhance the early-age properties of steel slag cement mortar and promote the resource utilization of metallurgical solid waste, in this study, nano-SiO₂ (KH-NS) was modified using a KH550 silane coupling agent. The hydration kinetics and microstructure evolution were systematically analyzed by means of a macroscopic performance test (setting time and compressive strength) and multi-scale microscopic characterization (characterized by Fourier Transform Infrared Spectroscopy, Scanning Electron Microscopy, X-ray Diffraction, Thermogravimetry-Differential Thermal Analysis, and isothermal calorimetry). The influence mechanism of its content on the early performance of the steel slag cement system was systematically studied. Research findings indicate that at a given dosage, increasing the proportion of KH-NS results in a shorter setting time for steel slag mortar. When the KH-NS dosage reaches 1.5%, the initial and final setting times of steel slag mortar decrease by 24.21% and 21.20%, respectively. The addition of KH-NS effectively enhances the compressive strength of mortar, with a particularly pronounced effect on early strength prior to 14 h of curing. At a KH-NS dosage of 1.5%, the onset of the accelerated phase of hydration heat release in steel slag cement mortar is advanced by 2.5 h. Mechanistic studies indicate that KH-NS accelerates cement hydration by promoting C₃S dissolution and C-S-H gel nucleation through interactions between surface silanol groups (Si-OH) and amino groups (-NH₂). Furthermore, KH-NS refines the pore structure via a micro-aggregate filling effect, reducing the number of harmful pores and improving the pore size distribution. KH-NS continuously consumes Ca(OH)₂ through pozzolanic reactions to generate C-S-H, with its reactivity increasing with higher dosage. Research confirms that KH-NS significantly enhances the early strength and density of steel slag mortar, providing both theoretical justification and technical support for developing low-carbon building materials based on solid waste with high dosage. Full article

The large-scale accumulation of steel slag from steelmaking and the over-exploitation of natural aggregates pose significant environmental and resource challenges. Focusing on the arid-cold region of Xinjiang, China, this study proposes the use of steel slag as a substitute for natural aggregates in pavement engineering. Through experimental performance evaluation and regionalized life cycle assessment (LCA), the technical feasibility and carbon reduction potential of this application were comprehensively evaluated. Results indicate that steel slag asphalt mixtures meet or exceed specification requirements in terms of high-temperature stability, water stability, and low-temperature crack resistance. However, volume stability decreases slightly with higher steel slag content and finer particle size, necessitating pretreatment for long-term durability. A local life cycle assessment model considering regional transportation factors was applied to the G30 Luhuo Expressway project. During the materialization stage, steel slag was used to replace 30% of the natural aggregates, reducing approximately 6718 kg of carbon dioxide equivalent emissions (31.4%). This, to some extent, reduced the extraction of natural resources, saved land resources, and alleviated the problems of resource shortage and price fluctuations. Sensitivity analysis reveals a positive correlation between carbon reduction and steel slag content, while transport distance strongly influences overall benefits, with a critical threshold of about 78 km defining the effective utilization range. Furthermore, a multi-objective optimization model balancing service life, cost, and carbon reduction was developed to identify an optimal steel slag content scheme, maximizing comprehensive benefits under constrained conditions. This work confirms the technical viability of steel slag pavement in extreme climates and provides a systematic framework integrating environmental benefits and logistical constraints, supporting regional industrial synergy and promoting circular economy practices in low-carbon infrastructure. Full article

Bothriochloa ischaemum is a key forage species with strong grazing tolerance and high nutritional value, making precise quantification of spike and seed traits essential for germplasm evaluation and yield prediction. However, the compact architecture and minute seed size in natural field conditions render manual counting inefficient and labor-intensive. To address this limitation, this study presents a non-destructive and automated quantification framework integrating advanced object detection and regression analysis for accurate in situ estimation of spikes and seed numbers. To further address the challenges of dense spike detection caused by occlusion and small object sizes, this study developed a modified model named YOLOv12-DAN by integrating DySample dynamic upsampling, ASFF feature fusion, and NWD loss, which achieved a mean average precision (mAP) of 91.6%. Meanwhile, for the detection of dense kernels on compact spikes, an improved YOLOv12 architecture incorporating an Explicit Visual Center (EVC) module was proposed to enhance multi-scale feature representation. The optimized model attained a bounding box precision of 96.5%, a recall rate of 86.4%, an mAP50 of 94.3%, and an mAP50-95 of 73.9%. Furthermore, a univariate linear regression model based on 132 spike samples verified the reliable consistency between the predicted and actual seed counts, with a mean absolute error (MAE) of 6.30, a mean absolute percentage error (MAPE) of 9.35, and an R-squared (R²) value of 0.808. Finally, the model was deployed through a lightweight end-to-end web application, enabling real-time field operation and promoting its applicability in breeding programs and agronomic decision-making. This study provides a robust technical pathway for automated phenotyping and precision forage improvement. Full article

The rapid rise of live-streaming e-commerce has fostered a new “content clipping” model, in which secondary creators edit and republish anchors’ live-streaming content to promote product sales. While this model can expand market reach and enhance revenue, it also introduces copyright disputes, regulatory challenges, and profit-sharing conflicts among platforms, anchors, and secondary creators. This study develops a three-party evolutionary game model to examine strategic choices regarding platform regulation, anchor authorization, and secondary content creation. Results reveal that excessive regulation may undermine equilibrium and profitability, while appropriate authorization can balance risk and reward. Secondary creators’ participation is sensitive to commission rates and cost–benefit trade-offs. This research contributes to the literature by integrating copyright governance into live-streaming e-commerce game theory and offers actionable insights for designing regulatory mechanisms, optimizing authorization policies, and fostering sustainable multi-party collaboration. Full article

Saline–alkaline water constitutes a vital strategic non-traditional fishery resource in China, characterized by high pH values, elevated carbonate alkalinity, and complex ionic compositions. These extreme environmental conditions impose significant stress on aquatic animals, mainly by inducing ionic toxicity and disrupting acid–base regulatory mechanisms. Such disruptions subsequently lead to osmotic imbalance, metabolic dysregulation, and immunosuppression, thus restricting the survival and growth of aquatic species in aquaculture systems. Consequently, the sustainable development of the saline–alkaline aquaculture is imperative for enhancing production efficiency and promoting the utilization of marginal land and water resources. This review comprehensively summarizes the current status of saline–alkaline aquaculture and highlights the stress-inducing impacts of salinity, alkalinity, and specific ionic ratios on teleost fishes and crustaceans. It further explores key adaptive mechanisms, including osmoregulatory and ionoregulatory strategies, bioenergetic trade-offs related to oxygen consumption and ammonia excretion, coordinated antioxidant and innate immune responses, as well as recent findings from multi-omics research. This review aims to offer a scientific foundation for the selection and breeding of saline–alkaline-tolerant strains, the precise regulation of aquaculture water environments, and the development of ecological aquaculture models in saline–alkaline regions, thereby facilitating the sustainable utilization of saline–alkaline land and water resources. Full article

Global climate change is reshaping Central European conifer forests, affecting growth and ecosystem dynamics. At the same time, tree species differ in their productivity and responses to climatic conditions. Across mid-elevation monocultures of European yew (Taxus baccata L.), Norway spruce (Picea abies [L.] Karst.), Scots pine (Pinus sylvestris L.), silver fir (Abies alba Mill.), and European larch (Larix decidua Mill.), we quantified stand structure, volume, biomass carbon sequestration, and growth–climate responses (1971–2023). Silver fir reached the highest stand volume (711 m³ ha⁻¹), with lower productivity in pine (−17.0%), larch (−22.9%), spruce (−26.0%), and yew (−70.6%). In contrast, larch maximised biomass carbon sequestration (267.7 t ha⁻¹), whereas yew had the lowest value (87.7 t ha⁻¹), but the greatest stand diversity (except high differentiation), while pine showed the lowest diversity. Radial growth was most constrained by warm Junes and dry Julys; an early-season multi-month drought compounded by heat further suppressed radial increments, and severe winter frosts added stress. Among the studied species, spruce was the most climate-sensitive, whereas fir and pine showed comparatively more resilience. From a practical forestry perspective, promoting structurally diverse stands with high production potential and prioritising climate-resilient tree species, especially fir, can help sustain production and stability at mid elevations under climate warming. Our results provide species-specific benchmarks for adaptive silviculture and identify the seasonal windows when growth is most vulnerable. Full article