Search Results (4,990)

Endophytes are a type of microorganism that lives in harmony with plants, playing a significant role in promoting the growth of the host and enhancing the host’s stress resistance. Understanding the ecological functions of root endophytic fungi and screening functional strains can effectively alleviate the stress conditions of crops. In this study, endophyte 1R13 was isolated from the roots of rice. Through morphological observation and five-gene combined phylogenetic analysis, it was identified as Bussabanomyces oryzae (B. oryzae), which was proposed as a new species, Bussabanomyces oryzae nov. The colonization pattern of B. oryzae was mainly through invasion of the rice roots, entering the epidermal cells and then the cortical cells, and finally reaching the vascular bundle cells. In the co-culture assays with rice, B. oryzae can promote the growth of rice, increasing its growth volume by approximately 23% and its fresh weight by 52%. Meanwhile, it could enhance the stress resistance of rice, mainly manifested as increasing the ability of rice leaves to resist rice blast and improving the survival rate of transplanted seedlings in the field. Full article

The purpose of this research is to examine the impact of industrial noise levels ranging from 74 to 76 dB—which fall below the legal limit of 80 dB—on complex physiological and psychological stress responses of workers. The study employs a multimodal approach, combining objective acoustic measurements according to the EN ISO 9612:2009 standard with the monitoring of physiological parameters, specifically galvanic skin response (GSR), blood pressure, and heart rate, complemented by subjective assessments through questionnaires. Key findings revealed that the C-weighted noise level L_CEX (r = 0.67) demonstrates a stronger correlation with stress response and heart rate (r = 0.66) than the standard A-weighted filter (L_AEX). Although noise explains only approximately 4% of heart rate variability (R² ≈ 0.04), providing indirect support for the multifactorial nature of stress, subjectively, 71% of workers expressed a need for noise reduction due to accompanying symptoms such as headaches and tinnitus. The highest level of cardiovascular load was consistently recorded at workstation SZ7. The results suggest that industrial noise may represent a contributing factor to psychosocial risk even at levels below regulatory limits. The results provide indirect support for the hypothesis that low-frequency noise (LFN) components play a role in psychosocial stress, suggesting the need for further investigation using detailed spectral analysis in the prevention of industrial psychosocial diseases. Full article

Low-velocity impacts during manufacturing and maintenance (e.g., tool drops) can induce barely visible impact damage in composite aircraft structures, motivating sensing-assisted approaches for rapid post-event assessment. This study proposes and validates a strain-based structural health monitoring framework for carbon-fiber-reinforced polymer (CFRP) panels by combining surface-mounted strain gauges with explicit finite element analysis (FEA). Drop-weight tests were con-ducted in accordance with ASTM D7136 using a 1.0 kg hemispherical impactor at drop heights of 250–400 mm. Three strain gauges were positioned at 1.25 mm, 32.5 mm, and 52.5 mm from the impact point to quantify the spatial attenuation of peak surface strain. The measured peak strains exhibited clear-dependent decay and increased with impact energy up to 350 mm, whereas the 400 mm case showed a non-monotonic response and a pronounced deviation from an elastic energy-scaling baseline, consistent with a transition to damage-dominated energy dissipation. Dedicated MSC Apex/Nastran Implicit simulations reproduced experimental trends and provided a physics-based digital twin for interpreting strain signatures in elastic regions, correlating them with likely damage states. Full article

Energy poverty remains an important challenge for sustainable development in China, with pronounced regional disparities and evolving temporal dynamics that require accurate and interpretable prediction tools. This study develops a provincial panel-based framework that combines Energy Poverty Index (EPI) construction, SSA-LSTM prediction, SHAP-based model interpretation, and two-way fixed effects (TWFE) regression analysis. Using provincial data for China (2003–2022), we first construct a composite EPI with the entropy weight method, then apply a Sparrow Search Algorithm (SSA) to optimize LSTM hyperparameters for EPI forecasting. SHAP is used to interpret feature contributions to model-predicted EPI, and TWFE regression is used to provide complementary panel-data evidence on factor–EPI associations. The results show that the SSA-LSTM model outperforms benchmark machine learning and deep learning models in out-of-sample prediction performance. SHAP-based interpretation indicates that variables such as GDP, energy intensity, and power generation per capita contribute strongly to prediction variation, with notable regional heterogeneity. TWFE results are broadly consistent with several key patterns identified in the SHAP analysis. Overall, the proposed framework provides an accurate and interpretable provincial energy poverty prediction approach and offers a useful empirical reference for energy poverty monitoring and policy discussion. Full article

Sustainable competitiveness has increasingly become an important policy objective for OECD countries, as economic performance is expected to be balanced with environmental protection, social well-being, and effective governance structures. The aim of this study is to evaluate and compare the sustainable competitiveness performance of OECD countries from a holistic perspective. In the analysis, six criteria reflecting the main dimensions of global sustainable competitiveness were considered. Criterion weights were calculated using the CRITIC (Criteria Importance Through Intercriteria Correlation) method, an objective weighting technique that does not rely on subjective judgments. These weights were then integrated with the CoCoSo (Combined Compromise Solution) method to rank the sustainable competitiveness performance of countries. In the final stage, a clustering analysis was applied to group OECD countries exhibiting similar sustainability characteristics. The findings indicate that natural capital emerges as the most influential dimension within the evaluation framework. According to the ranking results, Finland, Sweden, Lithuania, Denmark, and Estonia are positioned among the countries with the highest sustainable competitiveness performance. The results reveal noticeable differences across OECD countries, demonstrating that environmental, social, economic, and governance-related dimensions affect country performance in distinct ways. A direct comparison with the 2025 Global Sustainable Competitiveness Index shows a strong but not perfect association between the two rankings (Spearman’s ρ = 0.977), indicating structural consistency alongside meaningful mid-ranking shifts. Furthermore, the clustering results enable the identification of country groups sharing relatively similar sustainability profiles. Overall, the study contributes methodologically to the sustainable competitiveness literature by integrating objective weighting, multi-criteria decision-making, and clustering analysis within a unified analytical framework, while also offering insights for comparative policy evaluation. Full article

Metal–polymer hybrid joints are gaining importance as they combine high structural rigidity with a low weight. Additive manufacturing processes such as the laser powder bed fusion process (L-PBF) enable the production of complex metallic lattice structures that allow for form-fitting force transmission between the metal and polymer as mechanical interlock elements. In this work, metal–polymer hybrid compounds with additively manufactured transition zones are systematically investigated and mechanically evaluated. Three different lattice geometries (z4A, z8A, z8V) were fabricated from maraging steel (1.2709) using L-PBF and then hybridised with injection moulding using polypropylene (PP C7069-100NA). Mechanical characterisation was performed by tensile tests according to DIN EN ISO 527, in combination with statistical analyses and an analytical serial three-spring model to determine the homogenised elasticity modulus of the transition zone. The results show significant geometry-related differences in tensile strength, maximum force, and effective stiffness. The A-shaped transition zone geometry (z4A) achieves the highest mechanical performance and up to 82% of the tensile strength of the pure polymer, while the V-shaped transition zone geometry (z8V) has significantly lower load-bearing capacities. Variance analysis shows a dominant geometric influence with effect strength of η² ≈ 0.99. The analytically predicted stiffness values match the experimental results within 5–10%. This work demonstrates a reproducible, simulation-sparse approach to the analysis and design of metal–polymer hybrid connections. Full article

Introduction: Obesity is a well-established risk factor for numerous chronic diseases, including type 2 diabetes, cardiovascular disease, and certain cancers. Obesity-related complications can be managed through nutritional therapy, pharmacotherapy, and surgical interventions, each capable of achieving weight loss of over 10%. Understanding patient preferences and the factors that influence treatment choices is crucial to enhancing adherence and effectiveness. This sub-study aimed to identify the factors shaping patient preferences for nutritional therapies in the context of available pharmacological and surgical options. Methods: A participatory action study recruited 43 patients aged 18–75 years with a BMI greater than 35 kg/m² and obesity-related complications, including metabolic dysfunction, diabetes, hypertension, and chronic kidney disease. Participants viewed a 60-min informational video outlining treatment options before taking part in one-to-one interviews. Data were analysed using reflective thematic analysis. Results: This sub-study focuses on patients who expressed distinct attitudes toward nutritional therapy. Of the participants, 47% preferred nutritional therapy, 41% chose pharmacotherapy alone, and 6% selected a combination of pharmacotherapy and nutritional therapy. Five themes emerged to explain the preference for nutritional therapy: patient satisfaction, the personalised approach, effectiveness, empowerment, and side effects. Discussion: Nutritional therapies were still the most popular choice of many patients, suggesting there remain unmet needs of patients and that it should not be assumed that large majorities of patients with obesity only want pharmacotherapies or surgical therapies. Conclusion: Ensuring patients receive comprehensive information and regular guidance from nutritional experts is likely to further strengthen engagement. Full article

Although greenhouse vegetable production is rapidly shifting toward innovative soilless systems, soil-based conventional cultivation still dominates globally. This production system faces growing pressure to transition to sustainable practices. However, introducing biofertilisers into intensive systems often yields inconsistent results. Specifically, their effects on different lettuce traits vary due to complex relationships between genotype, biofertiliser, environmental conditions, and market demands. Single-parameter evaluations fail to balance conflicting criteria, necessitating multi-criteria decision-making (MCDM) methods for selecting optimal choices. This study aims to overcome these inconsistencies through an integrated fuzzy MCDM-based optimisation model. Three lettuce cultivars (‘Carmesi’, ‘Aquino’, and ‘Gaugin’) were grown in an unheated Surčin (Serbia) greenhouse during a 58-day autumn experiment using a complete block design. Four treatments were applied: a control (without fertilisation), effective microorganisms, a Trichoderma-based fertiliser, and their combination. Biofertilisers were applied before transplanting and four times foliarly during the vegetation period via battery sprayer. This defined 12 production models (cultivar–fertiliser pairs), evaluated across 10 criteria: agronomic (core ratio, number of leaves), quality (nitrate content, total antioxidant capacity, total soluble solids, and chlorogenic acid), sensory (overall taste, overall quality), and economic (total variable costs, total income). Four decision-making experts from the Faculty of Agriculture and the ready-to-eat salad industry assessed weighting coefficients using the fuzzy PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment) method. The fuzzy MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution) method was used to rank the alternatives. To confirm the stability of the obtained ranking with the fuzzy MARCOS method, we performed sensitivity analysis through 20 different scenarios. Applied fuzzy methods identified alternative A11—‘Aquino’ cultivar with combined biofertilisers—as the best-ranked option, followed by A6 and A7. This study validates fuzzy PIPRECIA and fuzzy MARCOS as effective tools for optimising lettuce production models. They support farmers in selecting the most favourable solution based on multiple criteria, aiding the shift from mineral fertilisers to sustainable biofertiliser-based systems in intensive production—especially helpful for producers making this transition. Full article

In the face of increasing pressure on agriculture and the effects of climate change, as well as the increasing popularity of Western foods, the enhancement of local food resources stands out as an essential strategy to strengthen food security and to slow down the erosion of traditions. Buprestid beetles are customarily consumed by some people in Togo, but these beetles remain poorly documented. This study explores the nutritional value of Buprestids consumed in Ecological Zone I of Togo. In total 630 individuals representing 7 ethnic groups from 14 localities, with 45 respondents in each, were interviewed. Buprestids were collected in the surveyed areas for identification, and specimens of Sternocera interrupta (Olivier, 1790) were specifically selected for biochemical analyses. Ash, amino and fatty acids, vitamins and fibre contents were determined using AOAC and AFNOR methods. Mineral content was determined by spectrophotometry; fatty acid composition by gas chromatography and amino acid composition by Biochrom 30+ analyzer. The results show that three species of Buprestids are still consumed in Togo, but that this practice is becoming increasingly rare among younger people. The decline can be attributed to a combination of ecological, sociocultural, and economic factors. The analysis of S. interrupta revealed the following composition (dry weight): 58.02 ± 0.18% protein, 6.63 ± 0.28% lipid, and 12.81 ± 0.49% fibre. From a micronutritional standpoint, the species is rich in vitamins and minerals, highlighting the need to conserve Buprestids for their nutritional value and role in food security amidst ecological transition, as well as their contribution to biodiversity. Full article

Accurate delineation of stream networks in low-gradient wetlands remains challenging due to subtle topographic variation and dense vegetation cover. This study systematically evaluated 48 Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) processing workflows through 1128 pairwise comparisons to identify optimal approaches for mapping fine-scale channels in Japan’s Kushiro Wetland, a Ramsar-designated ecosystem. The workflows combined three ground filtering methods (Progressive Morphological Filter, Cloth Simulation Filter, Multiscale Curvature Classification), four interpolation techniques (Inverse Distance Weighting, Triangulated Irregular Network, Kriging, Multilevel B-spline Approximation), two sink-filling algorithms (Planchon & Darboux; Wang & Liu), and two flow direction models (D8, D-infinity). Performance was first assessed using pixel-based Intersection over Union (IoU) metrics to quantify inter-method consensus. Independent plausibility-based validation was then conducted using near-contemporaneous Sentinel-2 imagery. Although pairwise statistical analysis identified workflows that achieved high inter-method consensus (median IoU = 0.90), external validation demonstrated that the CSF-MBA-Planchon-D8 workflow provided the most realistic presentation of optically observable channel corridors (validation IoU ≈ 0.85). These findings reveal that high inter-method agreement does not necessarily imply accurate landscape representation; multiple workflows may converge on systematically biased solutions. Ground filtering exerted the strongest influence on pairwise consensus, whereas plausibility-based validation highlighted the importance of selecting workflow combinations that preserve subtle channel morphology. Sink-filling and flow direction choices exerted comparatively minor effects in this low-gradient setting. The proposed dual-validation framework provides methodological guidance for wetland restoration planning and highlights the necessity of external validation in LiDAR-derived hydrological feature extraction. Full article

In this study, a series of SiO₂-based biomaterials synthesized via the sol-gel technique was developed by integrating different weight percentages (10_wt% and 15_wt%) of ferulic acid (FA) and varying weight percentages (6_wt%, 12_wt%, and 24_wt%) of polyethylene glycol (PEG). Chemical characterization of the materials was performed by FTIR-ATR spectroscopy to confirm the incorporation of the functional agents and the matrix structure. Biocompatibility was assessed through cell-based assays and gene expression analysis, highlighting a positive effect of the materials on cell proliferation and the regulation of key markers for tissue regeneration. Finally, the ability to induce hydroxyapatite (HA) formation was verified using simulated body fluid (SBF) following the Kokubo test, demonstrating the bioactive potential of the treated surfaces. The obtained results indicate that the combination of SiO₂, FA, and PEG via sol-gel represents a promising platform for applications in the field of bone regeneration and functional biomaterials. Full article

Global environmental issues are becoming increasingly severe, with climate change imposing varying degrees of economic impact on different cities. It is crucial for cities to pursue efficient, low-carbon, and sustainable development pathways to cope with climate change. Carbon emission efficiency (CEE) is an essential indicator for assessing their performance and progress toward low-carbon growth. However, traditional CEE assessments have yet to integrate regional differences in the socioeconomic costs of climate change. To fill this gap, we have built a combined efficient frontier Data Envelopment Analysis (DEA) model based on the weighted carbon emissions of each city’s climate costs to evaluate the CEEs of 252 cities in China from 2006 to 2021. Meanwhile, city classification and spatial Markov chains are used for spatio-temporal heterogeneity analysis, and finally, the efficiency is decomposed to determine the impact of different factors on carbon efficiency. The results indicate that the average CEE of coastal cities (0.57) is lower than that of inland cities (0.63), mainly due to higher climate costs and unbalanced development. In contrast, megacities and super-large cities in coastal areas have the highest CEE levels because of economies of scale and technological advantages. Efficiency decomposition shows that pure technical efficiency (PTE) is the primary driver of CEE differences, contributing 33.37% to inefficiency differences. Our findings emphasize the need for targeted, differentiated policies to address unique urban challenges. Green technology investments should be prioritized in areas with high emission reduction potential, while cross-regional technology diffusion mechanisms should be established in areas with medium reduction potential to foster innovation. Overall, this study could offer valuable insights into the sustainable and low-carbon transition of urban development. Full article

The aims of this research were to characterize neuromuscular control features within the gait cycle in Parkinson’s disease (PD) from the perspectives of muscle synergies and cross-modal coupling and to propose a joint representation of the relationship between muscle activation patterns and kinematic dynamic outputs. PD participants (n = 19) were included. Lower-limb surface electromyography (EMG) and kinematic dynamic channels, including pelvic/hip, knee, and ankle angular acceleration, were collected during level-ground natural walking. EMG signals were first decomposed using non-negative matrix factorization (NMF) to extract muscle synergies, and the number of synergies was evaluated using reconstruction performance (R²). Multimodal matrix factorization (MMF) was then applied to jointly decompose the EMG and angular-acceleration channels, yielding a cross-modal synergy representation comprising a shared temporal structure (H) and modality-specific weight structures (W): non-negativity was imposed on EMG weights, whereas kinematic weights were allowed to take positive and negative values to encode directional contributions. Under the current task and muscle set, NMF achieved high EMG reconstruction performance with four synergies (R² = 0.882). The synergy weights showed an ankle-dominant pattern: tibialis anterior (TA) consistently carried high weights across multiple synergies, while lateral gastrocnemius (LG) and soleus (SOL) contributed prominently to another synergy. The synergy activation profiles exhibited phase-dependent fluctuations with multiple rises and falls across the gait cycle, suggesting that synergy output was primarily characterized by continuous modulation rather than single-peak recruitment. MMF further identified eight cross-modal synergies, simultaneously capturing the shared contributions of key muscle groups (e.g., RF, TA, and SOL) and pelvic/hip and knee/ankle angular-acceleration channels within the same decomposition framework and summarizing their descriptive co-variation through the shared temporal structure (H). Overall, A low-dimensional synergy analysis combining EMG-only NMF with cross-modal MMF enables simultaneous characterization of cohort-level modular organization of muscle activity during gait and its descriptive association with pelvis-to-lower-limb dynamic output. This joint framework provides a methodological basis for quantitatively describing gait-related modular organization and temporal modulation patterns in this PD cohort under natural level-ground walking and lays the groundwork for subsequent testing of associations between synergy features and gait phenotypes, clinical severity, and rehabilitation responses. Full article

The proliferation of generative artificial intelligence (AI) as an autonomous recommendation agent fundamentally challenges traditional paradigms of marketing communication. As AI systems increasingly mediate consumer–brand relationships, understanding how artificial agents construct persuasive discourse—distinct from human communicators—becomes critical for developing effective dual-channel marketing strategies. Grounded in Source Credibility Theory and the Computers Are Social Actors (CASA) paradigm, this study investigates the semantic and structural divergence between AI-generated product recommendations and human influencer marketing messages in social commerce contexts. Employing a mixed-methods computational approach integrating term frequency analysis, TF-IDF weighting, Latent Dirichlet Allocation (LDA) topic modeling, and BERT-based contextualized semantic embedding analysis (KR-SBERT), we examined 330 Instagram influencer posts and 541 AI-generated responses concerning inner beauty enzyme products—a hybrid category combining functional health claims with hedonic beauty appeals—in the Korean social commerce market. AI-generated responses were collected through a systematically designed query protocol with empirically grounded prompts derived from actual consumer search behaviors, and analytical robustness was verified through sensitivity analyses across multiple parameter thresholds. Our findings reveal a fundamental divergence in persuasive architecture: human influencers construct experiential narratives exhibiting message characteristics typically associated with peripheral-route cues (sensory descriptions, emotional testimonials, social context), while AI recommendations employ systematic, evidence-based discourse exhibiting message characteristics typically associated with central-route argumentation (functional mechanisms, ingredient specifications, objective criteria). Topic modeling identified four distinct thematic clusters for each source type: human discourse centers on embodied experience and relational consumption, whereas AI discourse organizes around informational utility and rational decision support. Jensen–Shannon Divergence analysis (JSD = 0.213 bits) confirmed moderate distributional divergence, while chi-square testing (χ² = 847.23, p < 0.001) and Cramér’s V (0.312, indicating a medium-to-large effect) demonstrated statistically significant and substantively meaningful differences. These findings extend CASA theory by demonstrating that AI recommendation agents develop a characteristic “AI communication signature” distinguishable from human persuasion patterns. We propose an integrated Dual-Agent Persuasion Proposition—synthesizing CASA, ELM, and Source Credibility perspectives—suggesting that AI and human recommenders serve complementary functions across different stages of the consumer decision journey—a proposition whose predictions regarding sequential persuasive effectiveness and consumer processing routes await experimental validation. These findings carry implications for AI content strategy optimization, platform design, and emerging regulatory frameworks for AI-generated content labeling. Full article

Obesity disrupts skeletal muscle metabolism through insulin resistance, oxidative stress, and ectopic fat deposition, yet transcriptomic findings across individual studies remain inconsistent. We performed a meta-analysis of four independent RNA sequencing (RNA-seq) studies of human vastus lateralis muscle, comparing 29 individuals with obesity (body mass index (BMI) ≥ 30 kg/m²) and 23 with normal weight. Differential expression was analyzed using DESeq2, with age and sex included as covariates in studies where individual-level data were available. Study-level results were integrated using the direction-aware inverse normal method (weighted Stouffer). Between-study heterogeneity was assessed by gene-level I² statistics derived from random-effects meta-analysis of log₂ fold changes. Functional annotation was performed with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The weighted Stouffer method identified 2136 differentially expressed genes (DEGs) (adjusted p < 0.05), comprising 1028 upregulated and 1108 downregulated genes, of which 674 (31.6%) were detected only through the meta-analysis. Three genes—PHLDA3 (down), CNKSR2 (down), and SFRP4 (up)—were significant in every individual study and in the combined analysis. Downregulated DEGs were enriched in cytoplasmic translation, ribosomal structure, and oxidative phosphorylation, whereas upregulated DEGs were associated with extracellular matrix organization and the focal adhesion pathway. This RNA-seq meta-analysis of skeletal muscle in obesity identifies robust DEGs and dysregulated pathways, providing candidate targets for future mechanistic and translational research. Full article