Search Results (6,966)

Japonica rice offers high cost-effectiveness and yield, with the potential to replace glutinous rice in Chinese sweet rice wine (CSRW) brewing. It can be classified into aromatic and non-aromatic types, but whether different varieties cause flavor and metabolite differences in CSRW remains unclear. In this study, glutinous rice (GR), three aromatic japonica varieties (CS-217, HXR-450, SXJ-1018), and two non-aromatic varieties (TA-1, HR-1212) were used as raw materials. The qualities of different CSRWs were evaluated through physicochemical indices, sensory evaluation, phenolic and flavonoid contents, antioxidant capacities, HS-GC-IMS, and UPLC-MS/MS. The results showed that CS-217 displayed the highest total acid content, along with excellent overall sensory evaluation, total phenolic content, and antioxidant capacity. A total of 28 VOCs were identified by HS-GC-IMS, among which 13 compounds with VIP ≥ 1, including butyl isobutyrate, butyl acetate, and ethyl pentanoate, were identified as key flavor discriminant factors. Additionally, 2501 non-volatile metabolites were identified, and five key metabolic pathways were revealed. These pathways synergistically regulate CSRW flavor and nutritional quality. Different japonica rice varieties exhibited respective advantages in CSRW quality indicators, providing a basis for the diversification of raw materials in CSRW production. Full article

How does artificial intelligence (AI) adoption shape administrative efficiency and e-citizen integration in local governments, and what role does professional development play in mediating these relationships? Drawing on a survey of 500 municipal employees across Spanish municipalities, this study employs partial least squares structural equation modelling (PLS-SEM), with formal non-linearity testing via Warp3 algorithms, to test a theoretically grounded model. The conceptual framework integrates Digital Transformation Theory and Public Value Theory as primary explanatory lenses, while drawing on the Technology Acceptance Model (TAM) and Total Factor Productivity (TFP) logic as complementary background perspectives that contextualise rather than directly operationalise the micro-level findings. Structural results reveal that AI adoption exerts a strong direct (and statistically linear) effect on perceived administrative efficiency (β = 1.04, p < 0.001; the standardised coefficient exceeding 1.0 and R² > 1 are a legitimate WarpPLS warp-model fit index rather than evidence of model misspecification: the Warp3 warp functions inflate the variance of predicted efficiency and break the additive identity SST = SSM + SSE, with the high AI–PD collinearity (r ≈ 0.84) as the contributing mechanism (RSCR = 1.000, SSR = 1.000); a comparative re-estimation without the moderation term yields β = 0.87 and R² = 0.76; we adopt this parsimonious specification (β ≈ 0.87, R² = 0.76) as the substantively interpretable estimate, with predictive relevance confirmed by a high Stone–Geisser Q² = 0.685, indicating that the model fits and predicts well rather than overfitting, while simultaneously stimulating professional development (β = 0.84, p < 0.001, R² = 0.70). Professional development positively predicted both efficiency (β = 0.27, p < 0.001) and e-citizen integration (β = 0.26, p < 0.01). Efficiency is the primary driver of e-citizen integration (β = 0.54, p < 0.001, R² = 0.53). The proposed moderation of AI adoption by professional development on efficiency was not supported (β = −0.01, p = 0.44), suggesting additive rather than synergistic effects. Model fit was robust (GoF = 0.701; ARS = 0.749; APC = 0.495); convergent and discriminant validity were confirmed by composite reliability, average variance extracted, Fornell–Larcker, and HTMT criteria; and common method bias diagnostics (Harman’s single-factor test, full-collinearity AFVIF, and marker-variable analysis) indicated that systematic method variance was not a material threat. These findings offer micro-empirical evidence of the mechanisms linking AI adoption to citizen service outcomes via a professional development pathway and provide actionable recommendations for Spanish and European municipalities navigating AI-driven governance reform. Full article

Artificial intelligence (AI) is becoming increasingly embedded in the digital infrastructure of local government, creating new opportunities to improve public sector productivity while also influencing systemic risk and organisational resilience across interconnected public systems. As municipalities adopt AI to automate, support, and transform administrative processes, organisational performance becomes more dependent on the reliability of algorithms, the quality of data, effective governance, and coordination among public institutions. These growing interconnections create new vulnerabilities that can spread across public service networks, yet evidence on the productivity, risk, and resilience implications of AI adoption remains fragmented and dispersed across different fields of research. This study develops an integrative conceptual framework that examines the relationship between AI adoption, public sector productivity, systemic risk, and organisational resilience within interconnected sociotechnical systems. Drawing on insights from productivity economics, systems theory, and public governance, the framework positions total factor productivity (TFP) within a broader public value and risk governance perspective. Using the PRISMA 2020 methodology, the study systematically reviews 68 peer reviewed empirical studies published between 2015 and 2025, assessing productivity outcomes, methodological quality, effect sizes, and contextual factors relevant to local government and networked public administration. The findings show that productivity gains associated with AI are strongly influenced by organisational readiness, including digital maturity, workforce capabilities, governance quality, and institutional coordination. While AI has the potential to improve operational efficiency and strengthen adaptive capacity, inadequate readiness can increase systemic risks arising from algorithmic opacity, cybersecurity challenges, data dependence, coordination failures, and disruptions that may spread across interconnected administrative systems. The review also highlights that resilience depends on the ability of public organisations to anticipate, absorb, adapt to, and recover from AI-related disruptions while maintaining the continuity and quality of public services. The study contributes to theory by integrating perspectives from productivity economics, public administration, and systemic risk within a sociotechnical systems framework. It contributes empirically through a comprehensive synthesis of evidence on AI and public sector productivity and methodologically through the application of transparent PRISMA 2020 review procedures. From a practical perspective, the study offers a conceptual measurement framework and policy guidance for municipal decision makers seeking to improve productivity while strengthening resilience and reducing systemic risks in increasingly interconnected public governance systems. Full article

In highly variable estuarine environments, short-lived fishes must balance immediate reproduction against future growth, yet the reproductive role of maternal size remains poorly resolved. Here, we analyzed biological surveys of the yellowfin goby Acanthogobius flavimanus from Haizhou Bay, Yellow Sea, collected between 2023 and 2025, combining fecundity estimation, egg morphometry, histology, and size-structured reproductive-output modeling. Absolute fecundity scaled hyperallometrically with total length (β = 3.18, 95% CI: 3.05–3.31, R² = 0.71, p < 0.001), while mean egg diameter increased significantly with maternal size (R² = 0.65, p < 0.001), yielding an approximately 3.15-fold increase in estimated egg volume across the observed size range. Histology revealed contrasting ovarian developmental phenotypes within the same spring cohort: a precocious phenotype with active vitellogenesis and a delayed-development phenotype with structurally intact early-stage oocytes and higher condition factor. Relative Reproductive Output (RRO) modeling showed that females > 180 mm total length represented only 4.8% of abundance but contributed 18.5% of total standardized egg-production output under the assumptions of the model. Inter-annual monitoring further indicated a decline in mean body size and in the proportion of large individuals from 2023 to 2025, a pattern consistent with size truncation but requiring longer-term monitoring for causal attribution. These findings suggest that large females can contribute disproportionately to reproductive output even in a short-lived estuarine fish and support the inclusion of size structure in fisheries assessment and management. Full article

This study examines whether digital infrastructure contributes to sustainable development by improving green total factor productivity (GTFP)—a comprehensive measure that jointly evaluates economic output and environmental performance—in the Yangtze River Economic Belt. We exploit the staggered implementation of the “Broadband China” pilot policy as a quasi-natural experiment and estimate its effects using panel data for 107 prefecture-level cities from 2010 to 2022. The empirical strategy combines a staggered difference-in-differences design with an event study framework. The baseline results show that the average treatment effect for the full sample is positive but not statistically significant at conventional levels under standard TWFE estimation; however, the Sun–Abraham interaction-weighted estimator confirms a significant positive effect (ATT = 0.080, p < 0.05), and the Goodman-Bacon decomposition shows that the TWFE estimate is driven primarily by clean comparisons (91% weight, 0% negative weights). Further analysis reveals substantial regional heterogeneity. The estimated effect is significantly positive in the central region (0.171, p < 0.05), positive but not significant in the eastern region (0.097), and negligible in the western region (−0.042). A similar pattern emerges across income groups: digital infrastructure generates significant gains in GTFP in high- and middle-income cities, whereas the effect is not identifiable in low-income cities. These results remain robust to propensity score matching, placebo tests, alternative specifications, and alternative measures. Exploratory mechanism analysis provides limited evidence that technological innovation and industrial upgrading mediate the effect of digital infrastructure on GTFP within the sample period, though the causal interpretation of mediation is constrained by the sequential ignorability assumption. The findings suggest that the environmental returns to digital infrastructure depend on local complementary conditions, especially human capital, institutional capacity, and industrial foundations. These results imply that digital infrastructure policy should be differentiated across regions rather than implemented uniformly. By demonstrating that the environmental returns to digital infrastructure are conditional on local complementary conditions, this study contributes to the sustainability literature by providing a framework for quantifying and monitoring the sustainability impacts of digital infrastructure policies, with implications for sustainable development strategies in developing economies. Full article

To address the pronounced interlayer productivity disparity and uneven reserve utilization during the development of multilayer low-permeability porous carbonate gas reservoirs, the G gas field on the right bank of the Amu Darya River was selected as the study area. Core-parallel physical simulation experiments, orthogonal numerical simulations, and production logging test (PLT) data were integrated to investigate the mechanisms of interlayer interference and its key controlling factors under multilayer commingled production. The results show that interlayer interference is primarily controlled by the permeability contrast and production differential. With increasing permeability contrast, high-permeability layers contribute a larger proportion of total production, whereas the utilization of medium- and low-permeability layers declines, thereby intensifying interlayer interference. Under the same permeability configuration, the interference coefficient increases with increasing production differential. Moreover, compared with the two-layer commingled-production cases, the three-layer system showed a stronger response to pressure-differential variation. When the production differential increased from 1 MPa to 5 MPa, the interference coefficient in the three-layer system increased from 9.84% to 27.83%, indicating more pronounced productivity loss in the medium- and low-permeability layers. Orthogonal numerical simulation indicates that the sensitivity of the main controlling factors follows the order of production differential ≥ permeability ratio > thickness ratio > gas viscosity. PLT data further validate the reliability of the experimental and numerical simulation results. During the development of Well G-22, the XVac layer consistently dominated gas production, whereas the XVm and XVp layers acted as supplementary contributors, indicating a dynamic production pattern in which high-permeability layers are preferentially activated and medium- and low-permeability layers contribute progressively at later stages. These findings demonstrate that permeability heterogeneity is the fundamental cause of interlayer interference, while the production differential serves as an important amplifying factor. This study provides a theoretical basis for zonal production allocation, optimization of the production differential, and stable production management in multilayer low-permeability porous carbonate gas reservoirs. Full article

Phenylalanine ammonia-lyase (PAL) is the rate-limiting enzyme in the phenylpropane metabolic pathway, which is crucial for plant disease resistance. However, the functional roles of specific PAL members in lily defense against gray mold (Botrytis elliptica) remain unclear. Using the resistant lily cultivar ‘Sorbonne’, metabolomics analysis revealed that phenylpropane metabolites were significantly induced upon pathogen infection. Combined second- and third-generation transcriptome sequencing identified eight PAL family members. Among them, LhSorPAL1 and LhSorPAL2 were strongly induced by B. elliptica and were selected for further analysis. Both recombinant proteins exhibited PAL enzymatic activity catalyzing cinnamic acid production from L-phenylalanine. Overexpression of LhSorPAL1 or LhSorPAL2 in lily via Agrobacterium-mediated transformation had no obvious effect on plant growth but significantly increased the accumulation of lignin, flavonoids, and total phenols upon pathogen challenge, leading to enhanced resistance to gray mold. Conversely, antisense expression of LhSorPAL1 or LhSorPAL2 reduced the accumulation of these metabolites. Promoter analysis revealed that both LhSorPAL1pro and LhSorPAL2pro contain methyl jasmonate (MeJA)-, abscisic acid (ABA)-, and transcription factor-binding cis-elements. Collectively, these results demonstrate that LhSorPAL1 and LhSorPAL2 positively regulate lily resistance to B. elliptica by promoting phenylpropane metabolism, providing candidate genes for molecular breeding. Full article

Intelligent manufacturing has become a new driving force for the comprehensive green transformation and development of the lithium industry, representing both an intrinsic requirement and a strategic direction for promoting high-quality development in the sector. This study examines whether intelligent manufacturing can effectively enhance the green total factor productivity of the lithium industry from the dual perspectives of internal motivation and external pressure, based on relevant data from Chinese A-share listed lithium companies between 2010 and 2023. The study finds that: (1) Intelligent manufacturing can significantly enhance the green total factor productivity of the lithium industry. (2) Heterogeneity analysis indicates that the level of regional environmental regulations and the intensity of green competition within the industry are positively correlated with the extent of improvement in the lithium industry’s green total factor productivity. (3) Mechanism analysis reveals that intelligent manufacturing influences green total factor productivity through two pathways: green technological innovation and ESG disclosure. Furthermore, the intrinsic incentive effect of green technological innovation is stronger than the extrinsic pressure driven by ESG disclosure. (4) Further analysis reveals that the “Intelligent Manufacturing Pilot Project” policy and the “Comprehensive Green Transformation of Economic and Social Development” policy provide strong support and driving force for the intelligent manufacturing and green development of the lithium industry. Full article

Global climate governance is undergoing a rapid transformation, and energy systems are increasingly shifting toward low-carbon development. Against this background, improving manufacturing firms’ green total factor productivity (MFGTFP) is essential for achieving sustainable industrial development. China has introduced two major policy instruments: new energy demonstration cities (NEDCs) and low-carbon city pilots (LCCPs). NEDCs focus on optimizing the energy supply structure, whereas LCCPs seek to reduce carbon emissions through demand-side regulatory constraints. This study treated the joint implementation of NEDCs and LCCPs as a quasi-natural experiment and employed panel data from Chinese A-share listed manufacturing firms from 2007 to 2024. Using a multi-period difference-in-differences model and mechanism tests, we examined the effect of the joint implementation of these policies on MFGTFP. The empirical results show that the joint implementation of NEDCs and LCCPs significantly improves MFGTFP. This effect is more pronounced when NEDCs are introduced prior to LCCPs, particularly in cities with a higher government ecological governance capacity (GEGC) and in regions characterized by a lower carbon emission intensity (CEI). Mechanism analysis revealed that the joint effects of NEDCs and LCCPs operate through supply-side innovation and partially through demand-side cost-pressure channels. On the supply side, NEDCs promote green innovation (GI), thereby enhancing firms’ supply innovation. On the demand side, the evidence mainly reflects financing constraint (FC) alleviation rather than a positive capacity utilization (CU) channel. Together, these findings suggest that improvements in MFGTFP are driven by supply-side innovation incentives and partially by demand-side cost-pressure effects through FC alleviation. These findings provide firm-level evidence on how the joint implementation of energy and carbon policies promotes green productivity improvement. Full article

As green and sustainable development has become a central policy objective, identifying new drivers of urban green total factor productivity (GTFP) is of growing importance. This study examines whether data-element development is associated with improvements in urban GTFP and whether this relationship varies across different urban contexts. Using balanced panel data for 270 Chinese prefecture-level and above cities from 2011 to 2021, we construct a city-level data-element development index and employ a two-way fixed-effect framework to conduct the empirical analysis. The results show that data-element development is positively associated with urban GTFP, and this finding remains stable across a series of robustness checks. Further mechanism analyses provide evidence consistent with partial mediation through green technology innovation. The moderation analysis indicates that the GTFP-enhancing effect of data-element development is stronger in cities with higher levels of human capital. Heterogeneity analyses show that the positive effect is more pronounced in Eastern cities, higher-tier cities, and cities with stronger environmental regulation. The findings offer system-oriented policy implications for cities seeking to leverage data elements to improve GTFP, emphasizing coordinated governance across data circulation, human capital, green innovation conversion, and environmental regulation under differentiated urban conditions, thereby supporting more effective pathways for urban green productivity improvement. Full article

The cement industry is a major contributor to global carbon emissions. Therefore, reducing emissions while utilizing industrial wastes is critical for its sustainable development. Red mud, a solid waste byproduct of alumina smelting with main components like SiO₂, Al₂O₃, and CaO, can partially replace limestone as a raw material in cement production. TG-DSC thermal analysis clarified red mud’s three-stage weight loss characteristic during calcination (total weight loss rate of 22.11%), and orthogonal experiments identified calcination temperature as the core factor for its CaO content, with the optimal calcination pretreatment process confirmed (0.075–0.09 mm particle size, 1373 K, 1 h residence time, CaO content up to 21.1%). Based on the results, this study uses ANSYS Fluent 2021 R1 to simulate a TTF-type precalciner, establishing a validated multi-physical field model (all relative errors < 5%) to explore red mud blending ratios of 0%, 2.5%, 5%, 7.5% and 10%. Unlike previous experimental studies, this work uses a CFD model to quantify how red mud blending ratios affect the coupled thermo-chemical environment in a TTF precalciner, revealing a mechanism-driven trade-off among decomposition rate, CO₂, and NO_x that experiments alone cannot capture. Results show red mud slightly alters the internal temperature field and reduces the raw meal decomposition rate. The decomposition rate remains within the industrial acceptable range of 85–95% when the red mud blending ratio is no more than 5%, while further increasing the blending ratio to 7.5% and 10% causes the decomposition rate to drop below 85%. Therefore, a blending ratio of 5% is recommended, which balances waste utilization, decomposition rate, and emission reduction, providing solid technical support for red mud’s large-scale use in cement production. Full article

Egg production in Gallus gallus domesticus represents a complex, economically critical trait shaped by multiple interrelated phenotypes, including age at first egg, total egg number, egg weight, and clutch characteristics. These traits are governed by polygenic inheritance and modulated by environmental factors, making the dissection of their genetic architecture essential for improving breeding efficiency, particularly under the emerging “long-life layers” production model. This systematic review aimed to integrate current knowledge on the genetic and molecular basis of egg production traits through analysis of genome-wide association studies and related genomic approaches. A structured literature search identified 27 eligible studies, which were evaluated following PRISMA guidelines. Data extraction and meta-analysis were conducted using standardized genome annotations and computational pipelines. The synthesis of available evidence demonstrates moderate to high heritability for key reproductive traits and highlights consistent genomic signals across multiple chromosomes. Importantly, the findings reveal a shift toward a systems-level understanding of egg production, involving conserved biological pathways related to neuroendocrine regulation, folliculogenesis, and energy metabolism. The integration of diverse genomic approaches enables the development of more precise, breed-specific selection strategies. Overall, these advances support a transition from traditional selection toward molecularly informed breeding frameworks, with significant implications for productivity, sustainability, and global food security. Full article

This study aimed to characterize the spatial patterns of soil nutrients and labile carbon in the 0–20 cm surface layer of a red-soil bench-terrace hillslope during the first year following cut-and-fill engineering. Soil nutrient redistribution is classically conceptualized as upslope depletion and downslope enrichment, yet whether this paradigm holds after bench terracing remains poorly documente d. On a granite-derived red-soil hillslope in Yudu County, Jiangxi Province, China, we established three replicated transects across four slope positions in May 2025, one year after cut-and-fill bench terracing combined with Camellia oleifera–Pinus massoniana mixed young-forest restoration. The 0–20 cm surface layer was sampled for pH, organic matter, total nitrogen, total phosphorus, water-soluble organic carbon, particulate organic carbon (POC), and mechanical composition. The results showed that organic matter, total nitrogen, and POC all peaked on the upper slope, with enrichment factors of 8.8×, 3.8×, and 5.1× relative to the hilltop, respectively; the slope base did not function as a nutrient sink. Texture displayed a monotonic downslope differentiation but decoupled from the nutrient gradient, and pH was significantly negatively correlated with organic matter and POC. The observed short-term post-restoration non-classical pattern is best interpreted as the spatially heterogeneous footprint of subsurface exposure and localized topsoil redistribution during cut-and-fill engineering, overlain by one year of incipient biological input, rather than the product of modified erosion–deposition dynamics. POC appears to be a particularly sensitive tracer of early biological activity under these short-term post-restoration conditions when superimposed on a depleted inverted-surface baseline, and the pronounced spatial heterogeneity implies that precision management based on high-resolution spatial diagnosis is warranted to address the substrate patchiness inherited from cut-and-fill operations. However, the temporal scope of this one-year baseline survey limits the inference of long-term indicator performance, and follow-up monitoring is needed to confirm whether POC retains this sensitivity as the surface layer matures. Full article

Against the backdrop of global climate change and increasing pressure to mitigate carbon emissions, counties serve as critical units for urban–rural spatial development and carbon governance. However, their carbon emission performance (CEP) and underlying spatial mechanisms remain insufficiently understood. This study focuses on 157 counties in Henan Province, selecting three time points: 2013, 2018, and 2023. The study measures the CEP and analyzes its spatiotemporal differentiation characteristics. First, considering that carbon emissions are undesirable outputs generated during the economic production process, this study employs the undesirable output slack-based measure (UN_SBM) model and the super-efficiency slack-based measure model with undesirable outputs (Un_Super_SBM) to evaluate county-level carbon emission performance. Second, landscape pattern indicators, including expansion, complexity, and compactness, are selected, and regression models are constructed to explore the influence of different factors on carbon emission performance. The results show the following: (1) The overall CEP of counties in Henan Province improved from 2013 to 2023, but there were significant spatial differences. (2) Both “Total landscape area” (TA) and “Area-weighted mean shape index” (AWMSI) had significant positive impacts on CEP, whereas the “Splitting index” (SPLIT) inhibited CEP. (3) The effects of vegetation cover and transportation conditions varied, reflecting the heterogeneity of development stages and spatial functional positioning across different counties. This study reveals the relationship between urban–rural spatial form and carbon emission performance at the county level, providing empirical evidence for optimizing construction land spatial structure, enhancing CEP, and promoting regional low-carbon development. Full article

This study investigated the influence of different extraction solvents (ethanol and methanol) on antioxidant and oxidative stress parameters in 55 extra virgin olive oil samples obtained from various producers in the Aegean region of Türkiye. In this context, DPPH radical scavenging activity, total phenolic content (TPC), total antioxidant status (TAS), total oxidant status (TOS), and oxidative stress index (OSI) were determined in both extracts. Ethanol extracts showed higher TPC and DPPH radical scavenging activity, with mean DPPH values of 24.48% and 15.42% for ethanol and methanol extracts, respectively. Similarly, TOS and OSI values were higher in ethanol extracts, whereas TAS values were higher in methanol extracts. Correlation analysis revealed significant negative relationships between antioxidant parameters (TPC and TAS) and OSI, indicating an inverse association between antioxidant capacity and oxidative stress. Principal component analysis (PCA) demonstrated a distinct separation between antioxidant and oxidant variables. Bland–Altman analysis further confirmed systematic differences between extraction methods. Overall, the findings indicate that the extraction solvent is a critical factor in the determination of antioxidant and oxidative stress parameters in olive oil. Ethanol provides a higher phenolic content and radical scavenging activity, whereas methanol appears more effective in assessing total antioxidant capacity. The variability among samples reflects differences in production, processing, and storage conditions. Full article