Search Results (16,257)

The First Member of the Cretaceous Qingshankou Formation (K₂qn¹) in the Gulong Sag, Songliao Basin, contains vast shale oil resources conventionally interpreted as deposits of suspension settling in a quiescent, anoxic deep-lacustrine environment. However, this static “deep-lake” model fails to account for the strong lithofacies heterogeneity and high-energy sedimentary records observed in recently acquired core data. This study reconstructs the sedimentary dynamics of the K₂qn¹ shale through high-resolution core description, thin-section petrography, and flow-loop hydrodynamic simulations. We identify abundant sedimentary structures diagnostic of high-energy combined flows, including Hummocky Cross-Stratification (HCS), Swaley Cross-Stratification (SCS), erosional scour surfaces, and large-scale tabular intraclasts (up to 40 mm). Hydrodynamic simulations, utilizing an “equivalent substitution” method, demonstrate that the Minimum Vertical Suspension Velocity (V_mf) required to transport these large intraclasts exceeds 1.0 m/s. This threshold is 1 to 5 orders of magnitude higher than theoretical values derived from classical settling equations, confirming that the paleolake bottom was frequently perturbed by high-velocity storm-driven currents. Consequently, we propose an “Intermittent High-Energy Deposition Model,” wherein background suspension settling was punctuated by episodic storm events. We argue that these high-energy events facilitated organic matter enrichment through a “Transport-Burial Pump” mechanism, which operated in concert with the chemical stratification associated with the Oceanic Anoxic Event 2 (OAE2) to enable rapid physical burial and sealing of organic matter. These findings challenge the traditional fine-grained sedimentological paradigm and suggest that storm-reworked intervals—characterized by enhanced brittleness and hydrodynamic winnowing—constitute the primary “sweet spots” for lacustrine shale oil exploration. Full article

As a traditional dual-purpose ingredient for both medicine and food, the biomacromolecules in Panax ginseng include polysaccharides, pectin, exosomes, proteins and dietary fiber. Due to their unique chemical structures, physiological activities, and processing adaptability, these components have achieved diversified applications in the medical field, becoming one of the core raw materials for functional food development. Modern research shows that the biomacromolecules found in ginseng can regulate the body’s immunity, antioxidant and anti-tumor properties, as well as antibacterial properties and the ability to enhance the body’s metabolic capacity, demonstrating significant application potential in healthcare-related fields. Recent studies have found that in addition to the root, the stems, leaves, fruits and flowers of P. ginseng also contain various effective components such as ginseng polysaccharides and pectin, which have enhanced the utilization value of ginseng plant resources. Ginseng biomacromolecules can not only replace antibiotics but also improve the production performance of animals by influencing the structure of intestinal flora, providing raw materials for the selection and application of natural feed additives for animals. This review summarizes the latest research findings on the pharmacological properties and practical applications of ginseng-derived biomolecules. It primarily addresses the structural characteristics, pharmacological activities, and current applications in health and medicine of biomolecules such as ginseng polysaccharides, ginseng exosomes, ginseng proteins, and ginseng dietary fiber. It aims to provide a fresh perspective and a solid theoretical foundation for the in-depth development of ginseng in the fields of medicine and molecular biology. Full article

Background: Companion participation in medical consultations can influence the well-being of older adults with chronic illness, yet the mechanisms underlying this effect remain unclear. This study aimed (1) to examine how companion-reported value co-creation (coproduction and value-in-use) relates to patient-reported multidimensional well-being (psychological, existential, social, and physical), and (2) to test whether these associations vary according to patient and companion characteristics. Methods: A cross-sectional dyadic study of 907 patient-companion pairs (N = 1814) was conducted in Spain prior to the COVID-19 pandemic. Companions completed the adapted Spanish Value Co-creation Scale, while patients completed the McGill Quality of Life Questionnaire-Revised (MQOL-R). Construct validity was confirmed via confirmatory factor analysis, and structural equation modeling tested hypothesized relationships using robust maximum-likelihood estimation. Results: The model showed good fit (χ²/df = 2.41, CFI = 0.96, RMSEA = 0.041). Companion coproduction was positively associated with patient psychological (β = 0.32), social (β = 0.27), and existential well-being (β = 0.29), but not physical well-being. Value-in-use showed small negative associations (β ≈ −0.10 to −0.15), which may reflect relational strain arising when companions’ involvement is excessive or mismatched with patient needs. Coproduction effects were stronger among patients aged ≤75 years. Conclusions: Companion coproduction enhances key dimensions of patient well-being, highlighting its role as a relational resource in clinical practice. Conversely, higher companion value-in-use may signal potential relational strain. These pre-pandemic findings provide a baseline for post-COVID chronic care models that aim to actively involve companions and tailor support according to patient age. Full article

A systematic review of the scientific literature focused on the analysis of the main strategies related to the management of Eichhornia crassipes [water hyacinth] in Mexico is presented. The review was classified according to the levels of the biomass valorization pyramid, using specific inclusion and exclusion criteria: (i) eradication; (ii) physical control of the plant; and (iii) exploitation of its biomass. It focused on research conducted in Mexico from 1971 to 2023, which allowed for the analysis of a total of 147 studies. The results indicated that 73.4% of the research in Mexico is oriented towards the exploitation and valorization of water hyacinth biomass through the transformation of the plant into mid-value products, such as chemicals and materials (70.7%), energy (12.2%), food (7.3%) and health and lifestyle products (9.8%); however the sectors considered high-added-value, such as pharmaceuticals, fine chemicals and food, continue to be areas of opportunity. The remaining 26.6% focuses on control and eradication strategies, through the insertion of biological enemies, and physical control, by means of cutting and crushing the plant in situ. This situation underscores the importance of promoting integrated management approaches that incorporate biomass recovery, contributing to environmental sustainability and the responsible use of this invasive resource. Full article

This study provided county-level empirical evidence on how rural land institutional reform affected the urban–rural income gap, and offered policy implications for advancing rural revitalization and common prosperity. Utilizing panel data for 1380 counties in China from 2010 to 2020, this study treated the Market-Oriented Reform of Rural Collective Operational Construction Land in China (the Reform) as a quasi-natural experiment and used a multi-period difference-in-differences (DID) model as a quantitative approach to empirically examine the effect and underlying mechanisms of the Reform on the urban–rural income gap. The results indicated that: (1) The Reform significantly narrowed the urban–rural income gap and passed a set of robustness checks, with an average reduction of approximately 17.41%. (2) The Reform reduced the urban-rural income gap through multiple pathways, including “land supply expansion–value realization and appreciation”, “industrial structure upgrading–labor reallocation” and “efficient capital flows–infrastructure improvement”. (3) The narrowing effect of reform was more pronounced in eastern and western counties, counties with higher proportions of mountainous areas, and non-resource-dependent counties. (4) The Reform demonstrated diminishing marginal returns: the effect was larger in counties with wider initial urban–rural income gaps. In addition, more market-oriented land transfer methods were more conducive to land value realization. Accordingly, the government should advance the Reform prudently, adopt place-based implementation, promote two-way factor mobility, and improve benefit-sharing and regulatory mechanisms to sustain policy gains. Full article

Farmers’ adoption of green grain production practices is essential for advancing China’s ecological civilization and achieving carbon neutrality. However, adoption remains uneven because farmers’ decisions are embedded in local social structures and shaped by short-term economic incentives and constraints. Drawing on an embeddedness framework, this study investigates how social mobilization influences farmers’ green grain production practices, while also examining the moderating role of household resource endowments and the mediating role of non-market value perceptions. Using multi-stage survey data collected in Heilongjiang Province between June and September 2023, the results show that grassroots cadres foster farmers’ green production adoption through four dimensions of social mobilization—technical, knowledge, cultural, and relational embeddedness. Moreover, household endowments positively moderate these effects, and non-market value perceptions partially mediate the relationship between social mobilization and green production practices. These findings are robust to alternative model specifications. This study provides micro-level evidence on how a cadre-led, governance-based social mobilization process is associated with farmers’ adoption of green production practices. Overall, this study advances understanding of the behavioral foundations of farmers’ green transitions and highlights actionable policy levers for grassroots governance, helping translate external policy directives into internalized and sustainable production practices. Full article

This study presents a comparative Life Cycle Assessment (LCA) of packaging for 1000 kg of fresh-cut salad using conventional polypropylene (PP) and bio-based INZEA^® FH05 bags. Using the Product Environmental Footprint (PEF) methodology, the analysis revealed weighted scores of 152 ± 14 mPt for PP and 158 ± 14 mPt for the bioplastic. To distinguish between statistical significance and practical relevance, a Monte Carlo Analysis (MCA) was performed. Statistical significance was defined by the probability P(PP ≥ INZEA^®), representing the frequency of iterations where the PP impact was greater than or equal to the bioplastic, while practical relevance was assessed by the percentage difference (Δ) between mean values. The MCA demonstrates that the PP system is the environmentally preferable option in 91.5% of the simulated iterations; correspondingly, there is only an 8.5% probability (P) that PP results in a higher impact than the bioplastic. Despite this high frequency of preference, the overall tangible benefit remains modest, with a 4.1% reduction (Δ) in the total PEF score for PP. At the characterization level, bioplastic films showed a robust advantage in fossil resource depletion (P = 96.2%; Δ = +4.7% for PP); however, this was offset by significantly higher impacts in categories such as Acidification (P = 0%; Δ = −11.0%) and Freshwater Ecotoxicity (P = 0%; Δ = −29.7%). Conversely, the Climate Change category showed environmental parity with no tangible benefit (Δ = −0.5%) and a lack of statistical significance (P = 43%). These findings indicate that the theoretical benefits of compostability are currently hindered by industrial infrastructural deficiencies. Under current scenarios, PP maintains a competitive environmental profile, highlighting that a successful transition to bioplastics requires both material innovation and systemic improvements in waste management infrastructure. Full article

Lignocellulosic biomass is an abundant renewable resource, yet its effective utilization remains limited due to its structural recalcitrance, primarily attributed to lignin. While aerobic lignin-degrading microorganisms, particularly fungi, have been extensively studied, much less is known about bacteria capable of lignin depolymerization under low-oxygen conditions. This study focused on the isolation and evaluation of native anaerobic bacterial cultures capable of degrading lignin-derived compounds to enhance biogas production. Soil samples from decaying vegetation and olive mill wastewater were used as microbial sources. Enriched cultures were developed anaerobically using kraft lignin and p-coumaric acid as sole carbon sources. Twelve pure bacterial strains were isolated and screened for their ligninolytic activity. All strains were able to degrade p-coumaric, with the highest biomass concentration reaching 387 mg L⁻¹ and maximum substrate consumption rate at 438 mg L⁻¹ d⁻¹. When kraft lignin was used as sole carbon source, 9 out of 12 strains showed growth, with a maximum of 55 mg L⁻¹ over 11 days. Enzyme activity assays confirmed the production of lignin peroxidase and laccase, with highest values at 2.10 and 0.15 U mL⁻¹, respectively, even under conditions of limited oxygen. The enriched cultures were applied in biomethane potential (BMP) batch tests, resulting in increased methane production. The best performing culture resulted in a bioaugmentation percentage of 174% compared with control. These findings suggest that native ligninolytic bacteria can serve as promising bioaugmentation agents in anaerobic digestion of lignocellulosic waste. Full article

The growing accumulation of coal beneficiation waste represents a significant environmental and technological challenge while simultaneously creating opportunities for the resource recovery within circular economy frameworks. This study presents the development and process-oriented evaluation of an environmentally safe technology for converting coal beneficiation waste into potassium humate, with the simultaneous recovery of molybdenum compounds via alkaline extraction. The proposed solution is designed to improve resource efficiency, reduce the volume of waste directed to landfilling, and generate a high value-added product for agricultural and technological applications. The process flow includes preliminary characterization and preparation of the waste, determination of moisture, ash, and organic matter content, and the separation of metal-bearing fractions. Alkaline extraction was carried out using potassium hydroxide under controlled temperature and reaction time conditions, followed by purification and concentration of the humate solution. The process management strategy focuses on optimizing key technological parameters, including alkali concentration, solid-to-liquid ratio, temperature, and reaction time, to maximize humate yield while preserving functional groups responsible for biological activity. Comprehensive physicochemical, thermal, and mineralogical analyses confirmed the stability of the aluminosilicate matrix and the suitability of the material for alkaline processing without adverse structural degradation. Biological tests using oat (Avena sativa) demonstrated that potassium humate derived from coal beneficiation waste exhibits higher growth-stimulating effectiveness than a conventional commercial humate. Economic analysis revealed a strong correlation between humic acid content and added value, confirming the feasibility of transforming coal beneficiation waste from an environmental burden into a valuable secondary resource. Full article

Moringa oleifera (Lam.) is a multipurpose agroforestry tree cultivated worldwide for its nutritional, medicinal, and economic value, and it is increasingly grown commercially in subtropical regions, including Nepal. While vegetative propagation is feasible, large-scale production relies predominantly on seeds, making efficient germination critical for seedling establishment, uniform growth, sustainable production, and preservation of genetic diversity. Seed pre-treatments are widely recognized as a simple and effective approach to enhance germination, early seedling vigor, and nursery performance. This study evaluated the effects of seven pre-sowing treatments under controlled nursery conditions to determine the most effective method for improving Moringa oleifera seedling production. A total of 2100 seeds were used, with 100 seeds per treatment and three replicates, arranged in a Completely Randomized Design (CRD). Treatments included control (no pretreatment), normal water soaking (12 h and 24 h), alternating wetting (water) and drying cycles (12 h each), hot water soaking (60 °C for 5 min), cow urine soaking (1:2 of urine to water proportions for 12 h), and hydrochloric acid soaking (35% for 20 min). All pre-treatments were conducted at room temperature, and the seeds were subsequently sown in controlled nursery conditions. Seed germination was monitored twice daily for 30 days, and data were analyzed using one-way ANOVA and Tukey’s HSD test to identify significant differences in germination performances. Results demonstrated that alternating wetting and drying produced the highest germination percentage (89%), shortest mean germination time (8.44 days), and strongest seedling vigor, outperforming all other treatments. Conversely, cow urine and acid treatments completely inhibited germination. This study recommends alternating wetting and drying as a simple, low-cost, and chemical-free pre-treatment to optimize Moringa oleifera seedling production in nurseries. These findings provide practical guidance for commercial and smallholder farmers, contributing to sustainable agroforestry, food security, and climate-resilient livelihoods in resource-limited habitats. Full article

A growing body of literature argues in favor of the influence of organizational culture (OC) on firm performance (FP). Yet this consensus often emanates from studies that over-emphasize the direct culture–performance relationship, with methodologies that are deficient in revealing causal mechanisms and prone to giving ambiguous results. To address these gaps, this study proposes and tests an integrated theoretical framework, synthesizing the Schema Theory, Resource-Based View/Capability theory, and Contingency Theory of Firm Performance. This framework establishes a foundational influence mechanism of OC on performance, moving from cognitive schemas to actualized capabilities and environmental fit. Using data from 249 construction firms in Ghana, we employed a three-stage analytical process; using cluster analysis, we identified five cultural clusters, dominated by Clan and Adhocracy culture types (Organic cultures). Cross-tabulation revealed that large and resource-rich firms (D1K1 and D2K2) were more likely to exhibit balanced cultural profiles. Initial analysis using Kruskal–Wallis H Test showed no significant performance difference between balanced and organic clusters. However, when multiple regression was employed to control for firm classification and adverse industry conditions, the Balanced Culture profile emerged as a statistically significant predictor of superior performance. Consequently, we argue that while an Appropriate Culture, one dominated by organic traits and values, provides survival in a challenged environment, the Balanced Culture profile serves as a critical enabler of superior firm performance, once resource constraints and industry stressors are neutralized. Our findings hold particular importance for international–local joint ventures, where cultural alignment is a critical success factor. Additionally, the proposed framework establishes a robust theoretical foundation for future studies, especially those conceptualizing organizational culture as a foundational, independent variable. Full article

This study examines artificial intelligence (AI) not only as a student resource but as a pedagogical enabler—capable of operationalizing strategies such as context-based learning, narrative framing, and gamification that enhance motivation and relevance but are often difficult for instructors to sustain. By automating the generation of tailored scenarios, prompts, and examples, AI can make it feasible to embed these approaches consistently across large, multi-major classrooms. We applied this design in an undergraduate organic chemistry course for non-majors (N = 69) including Biomedical Laboratory Sciences, Nutrition, and Biology students. Organic chemistry for non-majors typically presents both conceptual challenges and low motivation due to limited career relevance, making this cohort well suited for examining AI-assisted pedagogies. Within this context, AI chatbot was integrated into chapter introductions, career-aligned scenarios, real-time activities, take-home assignments linking molecules to real-world contexts, and a game-based challenge—allowing the instructor to shift from sole source of personalization to a facilitator who guided and validated AI-generated materials. Surveys administered at the start and end of the semester revealed notable gains: student interest in organic chemistry increased from 42.0% to 73.3%, perceived relevance to majors rose from 24.6% to 85.0%, and importance for careers grew from 20.3% to 83.3%. Feedback after each activity indicated stronger awareness of real-life applications, greater confidence, and appreciation for AI’s role in making chemistry approachable. Students valued the clarity of introductions, the applied focus of the “Celebrity Molecules” assignment, and the engaging, collaborative nature of the game. Findings suggest AI can make evidence-based strategies more feasible and scalable, enhancing motivation and relevance in courses where students often struggle. Future work should examine long-term learning outcomes and transferability across disciplines. Full article

Surface defects in industrial production are complex and diverse. Therefore, deep learning-based defect detection models must consistently adapt to newly emerging defect categories. The trained models generally suffer from catastrophic forgetting as they learn new defect categories. To address this issue, we propose a selective distillation for incremental defect detection (SD-IDD) model based on GFLv1. Specifically, three selective distillation strategies are proposed, including high-confidence classification distillation, dual-stage cascaded regression distillation, and Intersection over Union (IoU)-driven difficulty-aware feature distillation. The high-confidence classification distillation aims to preserve critical discriminative knowledge of old categories within semantic confusion regions of the classification head, reducing interference from low-value regions. Dual-stage cascaded regression distillation focuses on high-quality anchors through geometric prior coarse filtering and statistical fine filtering, utilizing IoU-weighted KL divergence distillation loss to accurately transfer localization knowledge. IoU-driven difficulty-aware feature distillation adaptively allocates distillation resources, prioritizing features of high-difficulty targets. These selective distillation strategies significantly mitigate catastrophic forgetting while enhancing the detection accuracy of new classes, without requiring access to old training samples. Experimental results demonstrate that SD-IDD achieves superior performance, with mAP_old of 58.2% and 99.3%, mAP_new of 69.0% and 97.3%, and mAP_all of 63.6% and 98.3% on the NEU-DET and DeepPCB datasets, respectively, surpassing existing incremental detection methods. Full article

Background: Older adults with Alzheimer’s disease (AD) face a heightened risk of adverse hospital outcomes, including mortality. However, early identification of high-risk patients remains a challenge. While regression models provide interpretable associations, they may miss non-linear interactions that machine learning can uncover. Objective: To identify key predictors of in-hospital mortality among AD patients using both survey-weighted logistic regression and explainable machine learning. Methods: We analyzed hospitalizations among AD patients aged ≥60 in the 2017 Nationwide Inpatient Sample (NIS). The outcome was in-hospital death. Predictors included demographics, hospital variables, and 15 comorbidities. Logistic regression used survey weighting to generate nationally representative inference; XGBoost incorporated NIS discharge weights as sample weights during 5-fold hospital-grouped cross-validation and used the same weights in performance evaluation. Missing-value imputation and feature scaling were performed within the cross-validation pipelines to prevent data leakage. Model performance was assessed using AUROC, AUPRC, Brier score, and log loss. Feature importance was assessed using adjusted odds ratios and SHapley Additive exPlanations (SHAP). A sensitivity analysis excluded palliative care and DNR status and was re-evaluated under the same grouped cross-validation. Results: In the full model, logistic regression achieved AUROC 0.879 and AUPRC 0.310, while XGBoost achieved AUROC 0.887 and AUPRC 0.324. Palliative care (aOR 6.19), acute respiratory failure (aOR 5.15), DNR status (aOR 2.20), and sepsis (aOR 2.26) were the strongest logistic predictors. SHAP analysis corroborated these findings and additionally emphasized dysphagia, malnutrition, and pressure ulcers. In sensitivity analysis excluding palliative care and DNR status, logistic regression performance declined (AUROC 0.806; AUPRC 0.206), while XGBoost performed similarly (AUROC 0.811; AUPRC 0.206). SHAP corroborated the dominant signals from end-of-life documentation and acute organ failure in the full model; in the restricted model (excluding DNR and palliative care), SHAP highlighted physiologic and frailty-related features (e.g., dysphagia, malnutrition, aspiration risk) that may be more actionable when end-of-life documentation is absent. Conclusions: Combining regression with explainable machine learning enables robust mortality risk stratification in hospitalized AD patients. Restricted models excluding end-of-life indicators provide actionable risk signals when such documentation is absent, while the full model may better support resource allocation and goals-of-care workflows. Full article

Subsequent backfilling mining methods are critical technologies for the safe exploitation of deep metal mines, while the resource utilization of ultrafine tailings is a core component of green mining practices. This study focuses on the ultrafine tailings from the Tianxing iron mine to investigate the preparation and optimization of backfill slurry. The goal is to develop a low-cost, high-strength slurry suitable for large-scale preparation and long-distance pipeline transportation. The main findings are as follows: the 6920-type anionic flocculant was identified as the optimal agent, with an optimal dosage of 20 g/t, achieving an underflow concentration of 70.1% under dynamic testing conditions; a novel cementitious material (NCM) exhibited a 28-day uniaxial compressive strength of 3.14 MPa at a low binder-to-tailings ratio of 1:10, outperforming ordinary Portland cement and Slag Micro-powder; and the optimal slurry concentration was determined to be 70%, which provides a favorable balance between mechanical strength and flowability. Furthermore, economic analysis indicates that adopting NCM can reduce annual backfilling costs by approximately 13 million RMB. By establishing an integrated technical framework that includes “property characterization–flocculation optimization–binder selection–rheological regulation,” this study addresses key technical challenges associated with ultrafine tailings backfilling, significantly reduces binder consumption and overall backfilling costs, and provides precise parameter guidance for industrial-scale applications. The proposed approach demonstrates significant practical value for promoting green and sustainable mining development. Full article