Search Results (24,137)

This study develops an integrated panel econometric framework for modeling investment–output dynamics in circular economy sectors, explicitly addressing dynamic propagation, long-run equilibrium relationships, endogeneity, and nonlinear responses. Building on the Samuelson–Hicks Multiplier–Accelerator model, the analysis combines two complementary approaches. A dynamic panel specification estimated by the Generalized Method of Moments (Arellano–Bond) is employed to capture output inertia, intertemporal transmission of investment shocks, and stability properties of the dynamic system. In parallel, a nonlinear panel ARDL model estimated using the Pooled Mean Group (PMG/NARDL) methodology is used to identify cointegration and to distinguish between the long-run and short-run effects of positive and negative investment variations. The empirical analysis relies on a balanced panel of 28 European economies (EU-27 and the United Kingdom) over the period 2005–2023, using sectoral circular economy data, with gross value added as the output variable and gross private investment as the main regressor. The results indicate the existence of a stable cointegrated relationship between investment and output, characterized by significant asymmetries, with expansionary investment shocks exerting larger and more persistent effects than contractionary shocks. Dynamic GMM estimates further confirm delayed investment effects and a stable autoregressive structure. Overall, the paper contributes to mathematical economic modeling by providing a unified dynamic–equilibrium panel framework and by extending the empirical relevance of Multiplier–Accelerator dynamics to circular economy systems. Full article

The present study examined the impact of agricultural exports on economic growth in Lambayeque, Peru, during the period 2010–2023. An ordinary least squares (OLS) econometric model was employed to analyze the relationship between gross value added (GVA) and key macroeconomic variables, including agricultural exports, private investment, real wages, terms of trade, and the real multilateral exchange rate. The findings indicate that the model possesses considerable explanatory power (R² = 0.973) and that agricultural exports exert a positive and significant influence on regional GVA. In addition, private investment and real wages demonstrate positive elasticities, while terms of trade exhibit a negative relationship with regional economic growth. This highlights Lambayeque’s vulnerability to external price shocks. The study thus underscores the pivotal role of the Olmos Project, which has been instrumental in transforming arid land into fruitful agricultural zones through the implementation of an irrigation system encompassing over 22,000 hectares. This initiative has not only augmented agricultural exports, accounting for an impressive 90% of Lambayeque’s supply, but also contributed significantly to regional economic development by supporting employment generation and poverty reduction. Nevertheless, the presence of negative terms of trade indicates that the regional economy exhibits structural vulnerability in the face of external shocks. Notwithstanding the intrinsic limitations of regional, trade, and macroeconomic statistics, an understanding of the correlation between agro-exports and economic growth in a paradigmatic region of northern Peru provides substantial evidence for formulating policies to enhance the competitiveness and sustainability of the agro-export model. Full article

Exposure to lead is associated with microglial dysfunction and the development of neuroinflammation. This contributes to accelerated neurodegeneration. Even low doses of this element modulate inflammatory responses and might contribute to central nervous system dysfunction. Extracts from the mushroom Hericium erinaceus (HE) possess well-documented neurotropic properties; however, its potential neuroprotective mechanisms under conditions of environmental neurotoxicity remain poorly defined. In this study, we investigated the effects of HE on inflammatory signaling in a microglia-oriented in vitro model using THP-1-derived macrophages exposed to low levels of lead (3.5 µg/dL). In our study, Pb exposure did not increase tumor necrosis factor (TNF) alpha levels but reduced monocyte chemoattractant protein-1 (MCP-1) secretion and altered cyclooxygenase (COX) expression, indicating immune response modulation rather than inflammatory activation. Under combined Pb and HE exposure, a marked shift in cyclooxygenase expression toward COX-2 at both the gene and protein levels was observed, accompanied by increased PGE₂ production; these effects were dose-dependent. The inflammatory signaling was modulated rather than amplified. Also, TNF alpha levels were elevated after combined treatment, whereas gene expression responses were dose-dependent. MCP-1 secretion was fine-tuned toward control values, consistent with macrophage morphological changes, while IL-6 levels were increased. Overall, these findings indicate that Hericium erinaceus exerts immunomodulatory effects in microglia-like cells under low-level lead exposure, supporting its neuroprotective potential through modulation of neuroinflammatory signaling. Full article

Reliable estimation of crop chlorophyll status, a key indicator of photosynthetic activity and nutritional condition, is essential for supporting informed field management decisions. Recently, unmanned aerial vehicle (UAV) remote sensing has attracted considerable attention in crop chlorophyll estimation. However, research on integrating spectral indices (SI) with texture and structural information derived from high-resolution UAV imagery to estimate cotton chlorophyll remains limited, and the relative contributions of these different types of features are still unclear. This study utilized multispectral UAV imagery of cotton during the flowering stage at flight altitudes of 60 m, 80 m, and 100 m, from which the features of 12 SI, eight texture indices (TI), and four structural indices (STI) were derived. The Soil–Plant Analyzer Development (SPAD) provides an indirect yet relatively reliable assessment of leaf chlorophyll status. Accordingly, the Boruta algorithm was subsequently employed to identify variables that contribute significantly to SPAD-based estimation. For each flight altitude, SPAD estimation models were constructed based on three distinct machine learning algorithms. Subsequently, the SHapley Additive exPlanations (SHAP) framework was applied to determine key variables influencing SPAD estimation and to examine how the contributions of the three index types varied across different UAV flight altitudes. The results showed that combining UAV-derived SI, TI, and STI enables accurate estimation of cotton SPAD values. SHAP analysis further revealed the three feature types’ relative contributions to the RF model predictions. Among them, SI had the highest average model-attributed importance (59.36%), followed by STI (23.38%) and TI (17.25%). Moreover, with increasing UAV altitude, the importance of SI gradually increased, with its contribution rising from 58.79% at 60 m to 63.06% at 100 m; in contrast, the contribution of TI showed a decreasing trend, dropping from 20.42% to 12.82%. This study reveals the contributions of spectral, texture, and structural features to cotton SPAD estimation at different UAV flight altitudes, providing a clearer understanding of the relative roles of different feature types in cotton SPAD estimation. Full article

Driven by factors such as meteorology, topography, and industrial structure, the concentrations of volatile organic compounds (VOCs) exhibit significant spatial heterogeneity. Investigating the characteristics and sources of VOCs in different regions is therefore crucial for formulating targeted strategies to mitigate their contributions to fine particulate matter (PM_2.5) and ozone (O₃) pollution. This study comprehensively investigated—for the first time—the concentration characteristics, sources, and contributions to secondary organic aerosol (SOA) and O₃ formation of VOCs at an urban background site in Ganzhou, a southern Chinese city, based on hourly observations of VOCs during 2023. Analyses included ozone formation potential (OFP), secondary organic aerosol formation potential (SOAFP), and positive matrix factorization (PMF) source apportionment. The influence of photochemical loss was assessed using a photochemical age parameterization method. The results showed an annual average total VOC concentration of 22.6 ± 13.17 ppbv, with higher levels in winter and lower in summer. Alkanes were the dominant species (45.76%). After correcting for photochemical loss, the initial concentration of VOCs (IC-VOCs) was approximately 60% higher than the observed concentration of VOCs (OC-VOCs), with alkenes becoming the dominant group in IC-VOCs (≈72%). OFP analysis indicated that the OFP calculated using initial VOC concentrations (IC-OFP) was substantially higher (by 320 μg/m³) than the values calculated using observed VOC concentrations (OC-OFP), primarily due to the increased contribution of alkenes. SOAFP was higher in spring and winter, and lower in summer and autumn, with aromatic hydrocarbons being the dominant contributors (>85%). PMF results based on month-case studies identified combustion and industrial process sources as the major contributors (>20%) in August, while combustion and vehicle exhaust dominated in January. Photochemical loss significantly influenced source apportionment, particularly leading to an underestimation of biogenic emissions during a warm month (August). These findings underscore the necessity of accounting for photochemical aging and offer a scientific basis for refining targeted VOC control measures in Ganzhou and similar regions. Full article

As a prevalent integrated structure-insulation system, sandwich insulation wall panels have emerged as a critical structural configuration for zero- and nearly zero-energy green buildings, owing to their high construction efficiency and superior thermal insulation performance which directly aligns with the core goals of sustainability and sustainable energy utilization in the built environment. However, connectors penetrate the insulation layer and form systematic thermal bridges, which cause substantial heat loss and become a key bottleneck limiting further improvement in the overall thermal performance of wall systems. This study established three-dimensional numerical models of sandwich insulation wall panels with four typical connectors (fiber-reinforced polymers (FRPs), clamp-type stainless steel, plate-type stainless steel, and truss-type stainless steel) using Ansys Fluent 2021R1. The model reliability was verified by calibrated hot-box experiments, with relative errors between simulation and experimental results ranging from 2.1% to 16.1%. Systematic numerical simulations were then performed to investigate the effects of connector type, insulation material, climate zone, inner–outer temperature difference, connector quantity, and wall dimensions on the thermal bridge effect. The results indicated that FRP connectors caused the minimal heat flux increment (only 0.27%), followed by clamp-type stainless steel connectors (9.59%), while plate-type and truss-type stainless steel connectors led to significant increments (27.17% and 27.62%, respectively). The lower the heat transfer coefficient (K-value) of the wall was, the more prominent the connector-induced thermal bridge effect was. Within the typical temperature difference range, the heat flux increment of each connector remained stable, and polyurethane (PU) insulation exhibited a more significant inhibitory effect on thermal bridges than extruded polystyrene (XPS) under the same K-value. Linear fitting formulas for the relationship between wall K-value/temperature difference and the heat flux correction coefficient were derived, with high goodness-of-fit. The maximum impact of connectors on wall thermal performance did not exceed 30%. This study provides theoretical support and design references for the selection of connectors, material optimization, and thermal performance calculation of sandwich insulation wall panels, contributing to the promotion of energy-saving building envelope technologies. Full article

Background: Vancomycin is a critically important antimicrobial in human medicine, and vancomycin-non-susceptible enterococci represent a One Health concern when animal reservoirs contribute to the wider resistance ecology. We aimed to characterize vancomycin non-susceptibility among poultry-derived Enterococcus spp. from Hungary, using a combined phenotypic–genomic approach. Methods: Following a phenotypic pre-screen with antimicrobials authorized for poultry, 218 isolates with elevated minimum inhibitory concentrations (MICs) were selected for extended broth microdilution testing including vancomycin. Vancomycin susceptibility was interpreted using Clinical and Laboratory Standards Institute (CLSI) clinical breakpoints and European Committee on Antimicrobial Susceptibility Testing (EUCAST) epidemiological cut-off values (ECOFFs). Whole-genome sequencing was performed on a targeted multidrug resistant (MDR) subset (n = 42), enriched for elevated or borderline vancomycin MICs and stratified by region and host species (chicken, turkey), and resistance determinants were annotated against the Comprehensive Antibiotic Resistance Database (CARD) using stringent similarity/coverage thresholds. Results: Among the 218 pre-screened isolates (126 from chickens; 92 from turkeys), 196 (89.9%) met MDR criteria. For vancomycin, 15.6% of isolates were resistant and 9.2% intermediate by CLSI, while EUCAST ECOFF classification placed 34.9% in the non-wild-type group. The vancomycin MIC distribution was right shifted, with high-end MICs observed. In the sequenced subset, vancomycin-associated determinants consistent with the vanC pathway (including regulatory and auxiliary components) were detected in five isolates. Beyond vancomycin-related determinants, the WGS subset harbored common resistance genes consistent with the observed multidrug-resistant phenotypes. Conclusions: Vancomycin non-susceptibility was detected among pre-screened poultry-derived Enterococcus isolates in Hungary, and genomic analysis revealed vanC-associated and other peptide antibiotic resistance signatures. These findings support targeted One Health surveillance integrating MIC distributions with genomic resistance determinants in food animal reservoirs. Full article

Sonora state in Mexico, leads the nation in wheat production affected by various hydrometeorological phenomena, which can result in considerable economic losses. This research evaluates the potential relationship between emergency or disaster declarations associated with hydrometeorological events and wheat production from 2000 to 2024 in the Yaqui Valley aquifer in Sonora. This region alone contributed 51.6% of the total production value of Sonora in 2024. The results indicate that the issuance of declarations is consistent with losses and decreased wheat yields, resulting in a significant negative correlation (between r = 0.13 and r = 0.58) between the two variables. A total of 101 declarations were reported, with heavy rains being the primary cause at 44.6%. The municipality most affected was Guaymas, with 33 declarations from a total of 85. Additionally, 972 hectares were damaged in areas where declarations were issued, compared to 174 hectares damaged in areas where no declaration was made. These results provide a quantitative basis for the disaster risk diagnosis of wheat production in the Yaqui Valley, suggesting that the lack of records and timely information on hydrometeorological contingencies may result in a lack of awareness of the disruptive phenomenon, causing inconsistency between the failure to issue a disaster declaration and damaged areas, thereby increasing the vulnerability of the affected areas. Full article

Grade and cultivar are the important factors influencing white tea quality, but their relative metabolic contributions are not fully understood. Twelve white tea samples representing four major Fujian cultivars across three grades were analyzed using UHPLC–MS-based non-volatile metabolomics, HS-SPME–GC–MS volatile profiling, and sensory correlation analysis. In total, 47 non-volatile and 21 volatile markers were associated with grade differences, while 44 non-volatile and 26 volatile markers were linked to cultivar differences. Catechins and amino acids declined as grade decreased, whereas flavonol glycosides and gallic acid increased, accompanied by stronger astringency and reduced umami and sweetness. Aroma profiles showed a similar trend, with higher-grade teas dominated by floral notes and lower-grade teas exhibiting more herbal characteristics. Dimeric catechins, oxylipins, and aroma glycosides varied among cultivars. Volatile profiles separated the cultivars into two aroma groups: Fuding Dabai and Fuding Dahao showed more floral–fruity aromas, whereas Fuan Dabai and Zhenghe Dabai exhibited stronger herbal and aged aromas. Odor activity value analysis showed that linalool, geraniol, and (E,Z)-3,6-nonadien-1-ol were among the most abundant aroma-active compounds across white tea samples. These results provide chemical evidence for distinguishing white tea by grade and cultivar, with potential relevance to quality evaluation. Full article

Particulate Matter (PM) is a type of air pollution that poses risks to human health, the environment, and property. Among the various PM types, PM₁₀ is particularly significant, as it acts as a vector for numerous hazardous trace elements that can negatively impact human health and the ecosystem. Identifying potential sources of PM₁₀ and quantifying their impact on ambient concentrations is crucial for developing efficient control strategies to meet threshold values. Receptor modeling, which identifies sources using chemical species information derived from PM samples, has been widely used for source apportionment. In this study, PM₁₀ samples were collected over three periods (April, May, and June 2021), each lasting 16 days, using semi-automatic dust sampling systems at two sites in Biga, Canakkale, Turkiye. The relative contributions of different source types were quantified using EPA PMF (Positive Matrix Factorization) based on 35 elements comprising PM₁₀. As a result of the analysis, five source types were identified: crustal elements/limestone/calcite quarry (64.9%), coal-fired power plants (11.2%), metal industry (9%), sea salt and ship emissions (8.5%), and road traffic emissions and road dust (6.3%). The distribution of source contributions aligned with the locations of identified sources in the region. Full article

Professional experience placements are a requirement for undergraduate nursing students enabling real world skill development. Barriers to meaningful and positive placements have previously been reported, however there is limited research on how the location of placement impacts the student experience and outcomes. This study investigates the placement experiences of undergraduate nursing students at the University of Tasmania (UTAS) over a five-year period, with a focus on urban versus rural settings and year-level differences. Findings reveal that over one-third of students reported constructive placement experiences, with rural placements yielding slightly more positive outcomes than urban ones. First-year students were more likely to report constructive experiences compared to their senior counterparts, suggesting that longer placement durations may contribute to increased dissatisfaction. Quality of placement—defined by supervision and skill development—emerged as the most influential factor in shaping student experiences. While most students praised the quality of supervision, third-year students expressed both the highest praise and criticism. Opportunities for clinical and interpersonal skill development were central to students’ perceptions of placement quality, with rural placements slightly outperforming urban in skill development. However, some students, particularly in later years, felt that certain venues lacked adequate opportunities for skill acquisition. The study underscores the importance of high-quality supervision and appropriate clinical settings in enhancing placement experiences and suggests that constructive placements are more conducive to learning. These insights can inform strategies to improve the educational value of nursing placements across diverse settings. Full article

Fish screens are a sustainable agricultural innovation that offers economic and environmental benefits by protecting aquatic life and enhancing the efficiency of irrigation. In freshwater irrigation ecosystems, fish screens help protect aquatic organisms by reducing fish entrainment, facilitating ecological connectivity, and lowering mortality at early life stages. Therefore, they contribute significantly to aquatic biodiversity conservation. However, the role of trust in media in influencing Australian irrigators’ intentions to use fish screens remains underexplored. The study, guided by the Elaboration Likelihood Model (ELM) and incorporating the Theory of Consumption Values, examines trust in media as a persuasive factor impacting the functional, environmental, and Interpersonal Trust Cue of fish screens. The irrigators’ willingness to test, adopt, or implement fish screens can also predict the irrigators’ readiness to act for biodiversity-relevant outcomes. Data were collected between December 2021 and May 2023 from 192 Australian irrigators (sampling frame = 3736; response rate = 5.1%). The PLS-SEM results reveal that trust in media significantly predicts adoption intention (β = 0.134, 95% CI [0.021, 0.246]) and resource (time) efficacy (β = 0.170, 95% CI [0.054, 0.289]), with resource efficacy partially mediating this relationship. The study offers a theoretical contribution by integrating the ELM, the Theory of Consumption Value, and resource efficacy to explain how trust in media influences adoption through different persuasive routes. The model explains 22.5% of the variance in adoption intention. The findings indicate that resource efficacy is a critical enabling factor in translating conservation-oriented communication into an effective measure to protect freshwater biodiversity. Full article

The global economy is increasingly faced with the challenge of accepting its responsibility for recycling polyester textile waste. With back-to-monomer recycling technologies, PET can be recycled to its monomers, terephthalic acid and ethylene glycol. The recycling of polyester-containing textiles requires the complete separation of all contaminating materials, dyes, and additives, which can only be achieved by depolymerization technologies. This article presents the adsorptive decolorization of a disodium terephthalate solution from the alkaline hydrolysis of polyester textile waste. The influence of different adsorbents, temperature (30–80 °C), and pH value (7–12) on the adsorptive decolorization process is investigated. As a result, activated carbons for decolorization have been identified. It was found that the adsorption process is favorable at neutral pH and a temperature of 80 °C. The findings show that a color value within the industrial specification can be obtained for recycled terephthalic acid using activated carbon adsorption. This adds a key step for high-quality textile-to-textile recycling and thus contributes to a circular economy for polyester. Full article

As artificial intelligence (AI) is increasingly used in classrooms, tutoring systems, and learning platforms, it is essential that these tools are not only powerful, but also easy to understand, fair, and supportive of real learning. Many current AI systems can generate fluent responses or accurate predictions, yet they often fail to clearly explain their decisions, reflect students’ cultural contexts, or give learners and educators meaningful control. This gap can reduce trust and limit the educational value of AI-supported learning. This paper introduces the PEARL framework, a human-centered approach for designing and evaluating explainable AI in education. PEARL is built around five core principles: Pedagogical Personalization (adapting support to learners’ levels and curriculum goals), Explainability and Engagement (providing clear, motivating explanations in everyday language), Attribution and Accountability (making AI decisions traceable and justifiable), Representation and Reflection (supporting fairness, diversity, and learner self-reflection), and Localized Learner Agency (giving learners control over how AI explains and supports them). Unlike many existing explainability approaches that focus mainly on technical performance, PEARL emphasizes how students, teachers, and administrators experience and make sense of AI decisions. The framework is demonstrated through simulated examples using an AI-based tutoring system, showing how PEARL can improve feedback clarity, support different stakeholder needs, reduce bias, and promote culturally relevant learning. The paper also introduces the PEARL Composite Score, a practical evaluation tool that helps assess how well educational AI systems align with ethical, pedagogical, and human-centered principles. This study includes a small exploratory mixed-methods user study (N = 17) evaluating example AI tutor interactions; no live classroom deployment was conducted. Together, these contributions offer a practical roadmap for building educational AI systems that are not only effective, but also trustworthy, inclusive, and genuinely supportive of human learning. Full article

Background/Objectives: This study examined the effects of a computerized naming intervention combined with either cerebellar anodal transcranial direct-current stimulation (A-tDCS) or sham (S-tDCS) on noun and verb naming in Cantonese-speaking persons with chronic stroke-related aphasia (PWA). Methods: A double-blind, randomized, crossover, sham-controlled clinical trial was conducted with six Cantonese-speaking PWA following stroke. Participants received a 60 min computerized naming intervention incorporating audio–visual speech perception cues over five consecutive days, paired with concurrent 20 min of either 2 mA cerebellar A-tDCS or S-tDCS. Generalized linear mixed-effects models (GLMM) and linear mixed-effects models (LME) were used to evaluate naming accuracy and reaction time (RT). Individual variability was further explored through single-case analyses of naming accuracy changes across conditions and grammatical categories. Results: The GLMM showed a significant three-way interaction of condition, grammatical category, and time (p < 0.05). Specifically, the intervention paired with S-tDCS significantly improved verb naming, whereas A-tDCS did not induce significant improvements at the group level, effectively showing significantly smaller gains regarding verb naming compared to S-tDCS. Overall, RT decreased post-treatment across groups, but no significant differences emerged by the tDCS condition. The results support the promising efficacy of the Cantonese computerized audio–visual noun and verb naming therapy. Single-case analyses revealed high inter-individual variability in response to neuromodulation effects on naming and behavioral treatment outcomes. Conclusions: This study contributes to the emerging literature on cerebellar neuromodulation in post-stroke aphasia and underscores the need for larger trials examining grammar-specific (particularly verb-related) effects and polarity-dependent outcomes. It also highlights the value of developing personalized neuromodulation protocols to optimize the efficacy of behavioral language interventions in people with aphasia. Full article