Search Results (8,098)

Wood flooring manufacturers face complex challenges in dynamically allocating resources across multi-channel markets, characterized by channel conflicts, demand uncertainty, and long-term cumulative effects of decisions. Traditional static optimization or myopic approaches struggle to address these intertwined factors, particularly when critical market states like brand reputation and customer base cannot be precisely observed. This paper establishes a systematic and theoretically grounded online decision framework to tackle this problem. We first model the problem as a Partially Observable Stochastic Dynamic Game. The core innovation lies in introducing an unobservable market position vector as the central system state, whose evolution is jointly influenced by firm investments, inter-channel competition, and macroeconomic randomness. The model further captures production lead times, physical inventory dynamics, and saturation/cross-channel effects of marketing investments, constructing a high-fidelity dynamic system. To solve this complex model, we propose a hierarchical online learning and control algorithm named L-BAP (Lyapunov-based Bayesian Approximate Planning), which innovatively integrates three core modules. It employs particle filters for Bayesian inference to nonparametrically estimate latent market states online. Simultaneously, the algorithm constructs a Lyapunov optimization framework that transforms long-term discounted reward objectives into tractable single-period optimization problems through virtual debt queues, while ensuring stability of physical systems like inventory. Finally, the algorithm embeds a game-theoretic module to predict and respond to rational strategic reactions from each channel. We provide theoretical performance analysis, rigorously proving the mean-square boundedness of system queues and deriving the performance gap between long-term rewards and optimal policies under complete information. This bound clearly quantifies the trade-off between estimation accuracy (determined by particle count) and optimization parameters. Extensive simulations demonstrate that our L-BAP algorithm significantly outperforms several strong baselines—including myopic learning and decentralized reinforcement learning methods—across multiple dimensions: long-term profitability, inventory risk control, and customer service levels. Full article

The response of Arctic vegetation to climate warming exhibits pronounced spatial heterogeneity, driven partly by widespread permafrost degradation. However, the role of thermokarst lake development in mediating vegetation-climate interactions remains poorly understood, particularly across heterogeneous landscapes of northeastern Siberia. This study integrated multi-source remote sensing data (2001–2021) with trend analysis, partial correlation, and a Shapley Additive Explanation (SHAP)-interpreted random forest model to examine the drivers of normalized difference vegetation index (NDVI) variability across five levels of thermokarst lake coverage (none, low, moderate, high, very high) and two vegetation types (forest, tundra). The results show that although greening dominates the region, browning is disproportionately observed in areas with high thermokarst lake coverage (>30%), highlighting the localized reversal of regional greening trends under intensified thermokarst activity. Air temperature was identified as the dominant driver of NDVI change, whereas soil temperature and soil moisture exerted secondary but critical influences, especially in tundra ecosystems with extensive thermokarst lake development. The relative importance of these factors shifted across thermokarst lake coverage gradients, underscoring the modulatory effect of thermokarst processes on vegetation-climate feedbacks. These findings emphasize the necessity of incorporating thermokarst dynamics and landscape heterogeneity into predictive models of Arctic vegetation change, with important implications for understanding cryospheric hydrology and ecosystem responses to ongoing climate warming. Full article

Early life is a critical window for immune system development, during which the gut microbiome shapes innate immunity, antigen presentation, and adaptive immune maturation. Disruptions in microbial colonization—driven by factors such as cesarean delivery, antibiotic exposure, and formula feeding—deplete beneficial early-life taxa (e.g., Bifidobacterium, Bacteroides, and Enterococcus) and impair key microbial functions, including short-chain fatty acid (SCFA) production by these keystone species, alongside regulatory T cell induction. These dysbiosis patterns are associated with an increased risk of pediatric autoimmune diseases, notably type 1 diabetes, inflammatory bowel disease, celiac disease, and juvenile idiopathic arthritis. This review synthesizes current evidence on how the early-life microbiota influences immune maturation, with potential effects on the development of autoimmune diseases later in life. We specifically focus on human observational and intervention studies, where treatments with probiotics, synbiotics, vaginal microbial transfer, or maternal fecal microbiota transplantations have been shown to partially restore a disrupted microbiome. While restoration of the gut microbiome composition and function is the main reported outcome of these studies, to date, no reports have disclosed direct prevention of autoimmune disease development by targeting the early-life gut microbiome. In this regard, a better understanding of the early-life microbiome–immune axis is essential for developing targeted preventive strategies. Future research must prioritize longitudinal evaluation of autoimmune outcomes after microbiome modulation to reduce the burden of chronic immune-mediated diseases. Full article

Carbon capture is pivotal for achieving carbon neutrality; however, its high energy consumption severely limits the operational flexibility of power plants and remains a key challenge. This study, targeting a full flue gas carbon capture scenario for a 1000 MW coal-fired power plant, identified the dual-element (“steam” and “power generation”) coupling convergence mechanism. Based on this mechanism, a comprehensive set of mathematical model equations for the “carbon–electricity–heat” coupling process is established. This model quantifies the dynamic relationship between key operational parameters (such as unit load, capture rate, and thermal consumption level) and system performance metrics (such as power output and specific power penalty). To address the challenge of flexible operation, this paper further proposes two innovative coupled modes: steam thermal storage and chemical solvent storage. Model-based quantitative analysis indicated the following: (1) The power generation impact rate under full THA conditions (25.7%) is lower than that under 30% THA conditions (27.7%), with the specific power penalty for carbon capture decreasing from 420.7 kW·h/tCO₂ to 366.7 kW·h/tCO₂. (2) Thermal consumption levels of the capture system are a critical influencing factor; each 0.1 GJ/tCO₂ increase in thermal consumption leads to an approximate 2.83% rise in unit electricity consumption. (3) Steam thermal storage mode effectively reduces peak-period capture energy consumption, while the chemical solvent storage mode almost fully eliminates the impact on peak power generation and provides optimal deep peak-shaving capability and operational safety. Furthermore, these modeling results provide a basis for decision-making in plant operations. Full article

The impact and solidification of multiple molten droplets on a cold substrate critically influence the quality and performance of thermally sprayed coatings. We present a Smoothed Particle Hydrodynamics (SPH) model that couples fluid-solid interaction, wetting, heat transfer and phase change to simulate multi-droplet impact and freezing. The model is validated against benchmark cases, including the Young–Laplace relation, wetting dynamics, single-droplet impact and the Stefan solidification problem, showing good agreement. Using the validated model, we investigate two droplets—either centrally or off-centrally—impacting on a cold surface. Simulations reveal two distinct solidification patterns: convex pattern (CVP), which results in a mountain-like splat morphology, and concave pattern (CCP), which leads to a valley-like shape. The criterion for the two patterns is explored with two dimensionless numbers, the Reynolds number $Re$ and the Stefan number $Ste$. When $Re\le 17.8$, droplets tend to solidify in CVP; at higher Reynolds numbers $Re\ge 18.8$, they tend to solidify in CCP. The transition between the two patterns is primarily governed by $Re$, with $Ste$ exerting a secondary influence. For example, when droplets have $Re=9.9$ and $Ste=5.9$, they tend to solidify in a convex pattern, whereas at $Re=19.8$ and $Ste=5.9$, they tend to solidify in a concave pattern. Also, the solidification state of the first droplet greatly influences the subsequent spreading and solidification of the second droplet. A parametric study on CCP cases with varying vertical and horizontal offsets shows that larger vertical offsets accelerate solidification and reduce the maximum spreading factor. For small vertical distances, the solidification time increases with horizontal offset by more than 29%; for large vertical distances the change is minor. These results clarify how droplet interactions govern coating morphology and thermal evolution during thermal spraying. Full article

Pasture-based ruminant systems link nitrogen (N) nutrition with ecosystem N cycling. Grazing ruminants convert fibrous forages into milk and meat but excrete 65 to 80% of ingested N, creating excreta hotspots that drive ammonia volatilization, nitrate leaching, and nitrous oxide (N₂O) emissions. This review synthesizes ecological and ruminant nutrition evidence on N flows, emphasizing microbial processes, biological N₂ fixation, plant diversity, and urine patch biogeochemistry, and evaluates strategies to improve N use efficiency (NUE). We examine rumen N metabolism in relation to microbial protein synthesis, urea recycling, and dietary factors including crude protein concentration, energy supply, forage composition, and plant secondary compounds that modulate protein degradability and microbial N capture, thereby influencing N partitioning among animal products, urine, and feces, as reflected in milk and blood urea N. We also examine how grazing patterns and excreta distribution, assessed with sensor technologies, modify N flows. Evidence indicates that integrated management combining dietary manipulation, forage diversity, targeted grazing, and decision tools can increase farm-gate NUE from 20–25% to over 30% while sustaining performance. Framing these processes within the global N cycle positions pasture-based ruminant systems as critical leverage points for aligning ruminant production with environmental and climate sustainability goals. Full article

Covered karst collapse is a key geotechnical hazard in infrastructure construction in karst regions of China. In particular, strata consisting of an overlying clay layer and an underlying sand layer are prone to abrupt collapse induced by sand leakage under construction disturbances, which poses serious risks to pile foundation safety. To clarify the disaster-forming mechanism and develop a quantitative analysis method, this study investigates the mechanical behaviour of the entire collapse process by combining theoretical analysis with numerical simulation. A continuous mechanical analysis framework is established that follows the sequence from sand layer leakage to cavity expansion and then clay layer instability. Within this framework, a calculation model for the angle of repose of the sand layer is proposed that considers seepage and confined pressure effects. Simultaneously accounting for the influence of the casing, stability models for overall and localised collapses are developed using limit equilibrium theory. A comprehensive safety factor criterion K_c based on the critical span (or radius) is then proposed, leading to a linked evaluation method that couples the potential span of the sand layer with the ultimate span of the clay layer. The results show that an increase in Δh/h significantly reduces the angle of repose of the sand layer; the mechanical mechanism is confirmed whereby an increase in the roof span leads to shear stress exceeding the soil’s shear strength, thus triggering instability; the proposed safety factor K_c can effectively predict both overall and localised collapse, and case verification demonstrates that the predicted spans match well with actual collapse dimensions. The results provide a theoretical and technical basis for risk prediction, as well as for the prevention and control of pile foundation construction in karst areas. Full article

Background: This study assesses the robustness of a legacy N-glycan profiling method for the therapeutic antibody MAB1 with different Peptide-N-glycosidase F (PNGase F) enzyme sources, solid phase extraction (SPE) cartridges, and reagent stability, aligning with ICH Q14 lifecycle management principles. Glycosylation profiling is critical for therapeutic antibodies as it influences both function and pharmacokinetics. Method: The legacy N-glycan profiling method, 2-aminobenzoic acid (2-AA) labeling combined with normal-phase HPLC, was re-evaluated to confirm consistent analytical performance in the context of evolving regulatory expectations. The evaluation focused on three key factors: PNGase F enzyme sources, solid-phase extraction (SPE) cartridges, and reagent stability. Results: Commercial PNGase F enzymes showed various performances, with some sources yielding significant differences. Several SPE cartridges were also tested, with certain formats displaying poor recovery and high variability, particularly for sialylated glycans. In addition, reagent stability studies revealed rapid degradation of the labeling reagent within a few days. Conclusions: These results underscore the importance of risk control, continual improvement, and lifecycle management to ensure reliable glycosylation analysis and the sustained robustness of legacy methods. Full article

Gross Primary Productivity (GPP) plays a vital role in the terrestrial carbon cycle and ecosystem functioning. Understanding its spatio-temporal dynamics and driving mechanisms is critical for predicting ecosystem responses to climate change. China’s GPP has experienced complex responses due to heterogeneous climate, environment, and human activities, yet their impacts and interactions across ecosystems remain unquantified. This study used the Mann–Kendall test and SHapley Additive exPlanations to quantify the contributions and interactions of climate, vegetation, topography, and human factors using GPP data (2001–2020). Nationally, GPP showed a significant upward trend, particularly in deciduous broadleaf forests, croplands, grasslands, and savannas. Leaf area index (LAI) is identified as the primary contributor to GPP variations, while climate factors exhibit nonlinear interactive effects on the modeled GPP. Ecosystem-specific sensitivities were evident: forest GPP is predominantly associated with climate–vegetation coupling. Additionally, in coniferous forests, the interaction between anthropogenic factors and topography shows a notable association with productivity patterns. Grassland GPP is primarily linked to topography, while cropland GPP is mainly related to management practices and environmental conditions. In contrast, the GPP of savannas and shrublands is less influenced by factor interactions. These findings high-light the necessity of ecosystem-specific management and restoration strategies and provide a basis for improving carbon cycle modeling and climate change adaptation planning. Full article

This study provides the first comparative analysis of the physicochemical and functional properties of oil body suspensions derived from different parts—entire fruit (EOB), peel (POB), and seed (SOB)—of Idesia polycarpa Maxim (IPM) during in vitro simulated gastrointestinal digestion. Results demonstrated that the properties of the different suspensions exhibited significant difference during digestion stages. The average particle size of all suspensions decreased, with the most significant reduction observed in POB (91.50%), which was attributable to its lower interfacial protein content and inferior stability. The absolute ζ-potential decreased in the model of gastric digestion (MGD) due to interface disruption but increased in the model of intestinal digestion (MID) following the adsorption of bile salts. Throughout the simulated digestion process, the protein hydrolysis degree, free fatty acid (FFA) release rate, reducing power, and inhibition rates against α-amylase and α-glucosidase all increased, concurrently with a decrease in DPPH radical scavenging activity. Notably, the POB suspension exhibited the highest extent of lipid digestion, with the highest cumulative FFA release rate (27.83%). In contrast, the SOB suspension showed the most significant enhancement in total reducing power (increased by 199.32% after intestinal digestion) and the highest α-glucosidase inhibitory activity. These findings clarify that the part source is a critical factor influencing the digestive properties and functional activities of IPM oil bodies, providing a theoretical foundation for the targeted application in functional foods. Full article

Porphyromonas gingivalis (Pg), a keystone pathogen of chronic periodontitis, releases outer membrane vesicles (OMVs) that act as nanoscale vehicles to disseminate virulence factors within periodontal tissues and systemically beyond the oral cavity. Although Pg-OMVs are increasingly recognized as critical mediators of host–pathogen interactions, their effects on the differentiation and function of monocyte–macrophage/osteoclast lineage cells remain unclear. Here, we examined the impact of Pg-OMVs on the differentiation of RAW264.7 monocyte/macrophage-like cells into osteoclasts (OC) and/or macrophages (MΦ) in the presence of receptor activator of nuclear factor-κB ligand (RANKL). OMVs were isolated from Pg W83 and applied to RANKL-primed RAW264.7 cells using three distinct stimulation schedules: (1) simultaneous treatment with Pg-OMVs and RANKL at Day 0; (2) RANKL priming at Day 0 followed by Pg-OMV stimulation at Day 1; and (3) RANKL priming at Day 0 followed by Pg-OMV stimulation at Day 3. In all schedules, cells were cultured for 7 days from the initial RANKL exposure. Remarkably, simultaneous exposure to Pg-OMVs and RANKL (Schedule 1) markedly suppressed osteoclastogenesis (OC-genesis) while promoting M1 macrophage polarization. In contrast, delayed Pg-OMV stimulation of RANKL-primed cells (Schedules 2 and 3) significantly enhanced OC-genesis while reducing M1 polarization. These schedule-dependent effects were consistent with altered expression of osteoclastogenic markers, including dc-stamp, oc-stamp, nfatc1, and acp5. Importantly, a monoclonal antibody against OC-STAMP counteracted the Pg-OMV-induced upregulation of OC-genesis in Schedules 2 and 3. Furthermore, levels of Pg-OMV phagocytosis were inversely correlated with osteoclast formation. Finally, co-stimulation with RANKL and Pg-OMVs (Schedule 1) enhanced macrophage migratory capacity, whereas delayed stimulation with Pg-OMVs (Schedules 2 and 3) did not. Collectively, these findings indicate that Pg-OMVs exert stage-specific effects on the OC/MΦ lineage: stimulation at early stages of RANKL priming suppresses OC-genesis and promotes M1 polarization, whereas stimulation at later stages enhances OC-genesis without inducing M1 differentiation. Thus, Pg-OMVs may critically influence the fate of the OC/MΦ unit in periodontal lesions, contributing to disease progression and tissue destruction. Full article

Colostrum harbors a highly diverse microbial community, predominantly composed of genera such as Staphylococcus, Streptococcus, Lactobacillus, Bifidobacterium, and Enterococcus. The composition and diversity of this microbiota are influenced by maternal factors—including age, body mass index, lactation activity, stress levels, and gestational diabetes—as well as external factors such as mode of delivery, antibiotic exposure, diet, and geographic location. This microbial community plays a critical role in maternal and neonatal health by contributing to early gut colonization, supporting digestion, promoting immune system development, and protecting against pathogenic microorganisms through mechanisms such as antimicrobial peptide production by lactic acid bacteria. The primary aim of this study was to evaluate the impact of mode of delivery on colostrum microbiota by comparing mothers who delivered vaginally with those who underwent cesarean section. Colostrum samples from 15 mothers were subjected to DNA extraction, high-throughput sequencing, and bioinformatic analyses to characterize microbial composition and predicted functional profiles. Although substantial inter-individual variability was observed, no statistically significant differences were detected in overall microbial diversity or community structure between the two delivery groups. However, distinct bacterial taxa and functional characteristics were identified that were specific to each mode of delivery, suggesting subtle delivery-related influences on colostrum microbiota composition. Full article

Although green housing is widely regarded as an effective solution to energy and environmental challenges, its actual rate of adoption remains lower than expected. In the context of increasingly prominent sustainable development goals, promoting residents’ adoption of green housing has become a key issue in advancing sustainable transformation within the housing sector. Consequently, enhancing residents’ willingness to adopt green housing is critical to its broader diffusion. Drawing on diffusion of innovation theory, attitude theory, and perceived value theory, this study develops a multidimensional integrated model to identify factors influencing the adoption of green housing. The model examines how the innovation attributes of green housing and residents’ psychological evaluations jointly shape adoption intention. A questionnaire survey was conducted among 387 residents in Nanjing, China, and the data were analysed using partial least squares modelling. The results indicate that the five attributes derived from diffusion of innovation theory are significant antecedents of residents’ attitudes. Relative advantage, compatibility, trialability, and observability exert significant positive effects on residents’ attitudes toward adopting green housing, with relative advantage emerging as the most influential factor. Complexity has a negative, though comparatively weaker, effect on residents’ attitudes toward green housing adoption. Residents’ attitudes and perceived value are identified as significant predictors of green housing adoption intention. These findings contribute to a clearer understanding of residents’ green housing adoption intentions for both researchers and practitioners. More importantly, the study offers general policy and managerial implications for governments and developers seeking to enhance the uptake of green housing. Full article

Fatty liver in fish is characterized by excessive lipid accumulation, driven by factors such as inflammation, oxidative stress, and the overexpression of lipid-related genes. This condition can lead to metabolic dysfunction and reduced disease resistance, resulting in growth disorders and even mortality. Increasing incidence of fatty liver is closely linked to environmental conditions and feeding practices, posing significant challenges to the aquaculture industry. This paper offers a comprehensive overview of hepatic steatosis, with a particular emphasis on fish species. Through a detailed review of various scholarly works, this paper seeks to identify common patterns, emerging trends, and measurable correlations, highlighting the critical importance of understanding this complex relationship. The study of fatty liver is conducted across three dimensions: influencing factors, underlying mechanisms, and potential solutions. Currently, numerous factors contribute to the development of fatty liver, such as feed composition and environmental temperature. On a mechanistic level, the research explores lipid accumulation, inflammation, oxidative stress, and related processes. Furthermore, the paper suggests various solutions and preventive strategies, including considering environmental adaptability during animal migration, employing genetic enhancement techniques, modifying feeding practices, investigating the Nrf2 pathway, and utilizing rapamycin. These findings have significant implications for fisheries management and aquaculture practices, providing valuable insights to enhance sustainability in the industry. Full article

Circular economy and Industry 4.0 principles are increasingly shaping industrial practices. In the textile sector, environmental impacts and low recyclability make circularity a critical priority. This study focuses on enhancing both circularity and operational efficiency in a Portuguese manufacturer of labels and trimmings. Achieving this requires the collection of relevant data and identification of the factors that most influence operational performance, while linking these to circularity outcomes. To support this effort, the paper presents two complementary methodologies: Multi-layer Stream Mapping (MSM) for evaluating manufacturing efficiency and the Overall Circularity Index (OCI) for assessing circularity performance. MSM provides a detailed analysis of process efficiency, identifying sources of waste and summarizing results through user-friendly scorecards that highlight high-impact improvement areas. The OCI measures a company’s circularity on a scale from 0 to 1—where 1 represents full circularity—using strategic indicators across environmental, material, economic, and social dimensions. The MSM revealed an overall efficiency of 71%, whereas the OCI resulted in a final score of 0.516. When applied together, MSM and the OCI form a straightforward, iterative, and effective framework for diagnosing strengths and weaknesses in the manufacturing process, supporting evidence-based decision-making and guiding the company’s transition toward more circular and efficient operations. Full article