Search Results (16,843)

ERF family transcription factors are crucial regulators in plants, playing a central role in abiotic stress responses and serving as important targets for stress-tolerant crop breeding. Populus davidiana × P. bolleana, an elite hybrid poplar cultivar artificially selected in northern China, holds significant research value encompassing ecological restoration, economic industries, genetic resource development, and environmental adaptability. This study identified that PdbERF109 expression was significantly upregulated in P. davidiana × P. bolleana response to salt treatment. Furthermore, transgenic poplar lines overexpressing PdbERF109 (OE) were generated. Salt stress assays demonstrated that PdbERF109 overexpression significantly enhanced salt tolerance in transgenic poplar. Compared to wild-type (WT) plants, PdbERF109-OE lines exhibited a significant enhancement in the activities of antioxidant enzymes, with increases of 2.3-fold, 1.2-fold, and 0.5-fold for superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), respectively, while the levels of malondialdehyde (MDA) and hydrogen peroxide (H₂O₂) were markedly reduced by 39.89% and 40.03%, indicating significantly enhanced reactive oxygen species (ROS) scavenging capacity and reduced oxidative damage. Concurrently, PdbERF109 overexpression reduced the natural leaf relative water loss (%). Meanwhile, yeast one-hybrid assays confirmed that the PdbERF109 protein specifically binds to GCC-box and DRE cis-acting elements. This study established PdbERF109 as a positive regulator of salt stress responses, highlighting its potential as a target gene for improving plant tolerance to high salinity, providing a promising candidate gene for the molecular breeding of salt-tolerant crops. Full article

Terephthalic acid (TPA), a major monomer of polyethylene terephthalate (PET), represents a significant challenge in plastic waste management due to its persistence in the environment. In this study, we report a newly developed bacterial consortium capable of using TPA as the sole carbon source in a defined mineral medium. The consortium achieved stationary phase within five days and metabolized approximately 85% of the available TPA. Metabolite analysis by high-performance liquid chromatography (HPLC) and liquid chromatography tandem mass spectrometry (LC-MS/MS) revealed the activation of the benzoate degradation pathway during TPA catabolism. Additionally, the consortium secreted commercially relevant metabolites such as cis,cis-muconic acid and catechol into the culture medium. Genetic profiling using a reverse transcription quantitative polymerase chain reaction (RT-qPCR) and 16S rRNA sequencing identified Paraburkholderia fungorum as the dominant species, suggesting it plays a key role in TPA degradation. The ability of this microbial community to efficiently convert TPA into high-value by-products offers a promising and potentially economically sustainable approach to addressing plastic pollution. Full article

Qilian Mountain National Park, an important forest ecosystem in northwest China, plays a crucial role in achieving the national “dual carbon” goals and advancing sustainable forest management. This study focuses on the systematic assessment of forest carbon storage and its market economic value, employing multi-source data fusion and the GARCH-M(1,1) model to integrate forest carbon storage data from 2000 to 2020 with historical trading records from the EU and Chinese carbon markets (2017–2025). The study utilizes three dynamic carbon pricing scenarios—low, medium, and high—to assess the carbon storage capacity and economic value of the park’s forest ecosystems. Results show that forest carbon storage increased by approximately 4.0 × 10⁷ tons, with an average annual growth rate of 0.27%. Under the high carbon pricing scenario in 2025, the forest carbon sink value in the EU market reaches CNY 518.2 billion, approximately 12.5 times that of the Chinese market, highlighting the differences in market maturity and volatility persistence. Through Monte Carlo simulations and dynamic pricing analysis, this research reveals the substantial market potential of Qilian Mountain’s forest carbon sinks, providing data-driven support for regional carbon trading optimization, ecological compensation mechanisms, and sustainable forest management, while contributing to the global carbon trading system and international cooperation in forest-based climate mitigation. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

The transition to low-carbon energy systems requires efficient hydrogen production methods that minimise CO₂ emissions. This study presents a techno-economic assessment of hydrogen production via methane pyrolysis, utilising a novel liquid metal bubble column reactor (LMBCR) designed for CO₂-free hydrogen and solid carbon outputs. Operating at 20 bar and 1100 °C, the reactor employs a molten nickel-bismuth alloy as both catalyst and heat transfer medium, alongside a sodium bromide layer to enhance carbon purity and facilitate separation. Four operational scenarios were modelled, comparing various heating and recycling configurations to optimise hydrogen yield and process economics. Results indicate that the levelised cost of hydrogen (LCOH) is highly sensitive to methane and electricity prices, CO₂ taxation, and the value of carbon by-products. Two reactor configurations demonstrate competitive LCOHs of 1.29 $/kgH₂ and 1.53 $/kgH₂, highlighting methane pyrolysis as a viable low-carbon alternative to steam methane reforming (SMR) with carbon capture and storage (CCS). This analysis underscores the potential of methane pyrolysis for scalable, economically viable hydrogen production under specificmarket conditions. Full article

Background: Sacubitril/Valsartan is a first-line treatment for heart failure with reduced ejection fraction (HFrEF) according to international guidelines. However, achieving the target doses of guideline-directed medical therapy (GDMT) remains a challenge in clinical practice and its efficacy at suboptimal dose (<200 mg/day) versus angiotensin-converting enzyme (ACE) inhibitors remains debated. Our objective was to evaluate the titration of Sacubitril/Valsartan within 3 months of hospital discharge in patients with HFrEF. Methods: A cross-sectional study was conducted in a secondary care hospital in Geneva, Switzerland. Patients hospitalized between 2020 and 2022 with HFrEF, discharged with Sacubitril/Valsartan, were included. Physicians managing patients discharged with a Sacubitril/Valsartan dose of less than 200 mg/day were contacted and asked to complete a structured 7-item questionnaire regarding dose adjustments within the first 3 months following hospital discharge. The primary outcome was the proportion of patients who did not achieve GDMT doses of Sacubitril/Valsartan, along with reasons for inadequate titration. Results: Overall, 30 patients out of 79 (38%, 95% confidence interval [27–49%]) had not been titrated to an effective dose of Sacubitril/Valsartan 3 months after hospitalization. Of these thirty patients, the primary reason for not titrating cited by their practitioners (n = 27) was that titration was perceived to be within the cardiologist’s scope of responsibility (15/27, 56%). While most physicians (66%) knew the target doses for Sacubitril/Valsartan, 83% of them were unaware that the clinical benefit of sacubitril/valsartan at doses below 50% of the target compared to ACE inhibitors remains uncertain and is not well supported by current evidence. Conclusions: In this cohort, more than a third of patients with HFrEF were not titrated to guideline-recommended target doses of sacubitril/valsartan within 3 months of hospital discharge. This finding raises questions about the clinical and economic value of initiating sacubitril/valsartan without subsequent dose optimization, especially given the uncertainty surrounding the efficacy of suboptimal dosing compared to ACE inhibitors. Full article

Against the backdrop of urban–rural integrated development, special ecological function zones, as spatial carriers with significant regional ecological value and rural development functions, are confronted with a striking conflict between ecological conservation and regional advancement. This contradiction is comprehensively reflected in the interactions among land use functions (LUFs) that differ in nature and intensity. Therefore, exploring the trade-off and synergy (TOS) among regional LUFs is not only of great significance for optimizing territorial spatial patterns and advancing rural revitalization but also provides scientific evidence for the differentiated administration of regional land use. Taking 185 townships in the Funiu Mountain area of China as research units, this study constructs a land use assessment system based on the ‘Production–Living–Ecological’ (PLE) framework, utilizing multi-source datasets from 2000 to 2020. Spearman correlation analysis, geographically weighted regression (GWR), and bivariate local spatial autocorrelation methods are employed to examine the spatio-temporal dynamics of LUFs and the spatial non-stationarity of their TOSs. The findings indicate that, throughout the research period, the production function (PF) displayed a fluctuating declining trend, whereas the living function (LF) and ecological function (EF) demonstrated a fluctuating increasing trend. Notably, EF held an absolute dominant position in the overall structure of LUFs. This is highly consistent with the region’s positioning as a special ecological function zone and also a direct reflection of the effectiveness of continuous ecological construction over the past two decades. Spatially, PF is stronger in southern, eastern, and northern low-altitude townships, correlating with higher levels of economic development; LF is concentrated around townships near county centers; and high EF values are clustered in the central and western areas, showing an opposite spatial pattern to PF and LF. A synergistic relationship is observed between PF and LF, while both PF and LF exhibit trade-offs with EF. The TOSs between different function changes demonstrate significant spatial non-stationarity: linear synergy was the primary type for PF-LF, PF-EF, and LF-EF combinations, but each combination exhibited unique spatial characteristics in terms of non-stationarity. Notably, towns identified as having different types of trade-off relationships in the study of spatial non-stationarity are key areas for township spatial governance and optimization. Through the allocation of regional resources and targeted policy tools, the functional relationships can be adjusted and optimized to attain sustainable land use. Full article

Bacterial diseases of Pleurotus pulmonarius, caused by diverse pathogens and associated with a range of symptoms, reduce its commercial value and lead to substantial economic losses. While most research has focused on Pseudomonas tolaasii and its non-volatile toxin tolaasin, little is known about other bacterial pathogens and their volatile metabolites. In this study, two bacterial pathogens were isolated from symptomatic P. pulmonarius fruiting bodies in Guangxi, China, and identified as Ewingella americana and Cedecea neteri. Using headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS), we identified 16 volatile organic compounds (VOCs) produced by these two species, seven of which exhibited toxicity-inducing sunken lesions, discoloration, and inhibition of mycelial growth. Symptom severity was quantified by colorimetric analysis. Among the toxic VOCs, 2,4-di-tert-butylphenol was the most potent, inducing sunken lesions and slight discoloration at concentrations as low as 0.5 mg/mL, and causing significant inhibition of mycelial growth at 5 μg/L. The remaining VOCs also caused varying degrees of sunken lesions, yellowing or browning, and suppression of mycelial growth. This study is the first to demonstrate the pathogenic potential of VOCs produced by bacterial pathogens in P. pulmonarius, underscoring their role as important virulence factors and providing a foundation for further investigation into their mechanisms and control strategies. Full article

Gray mold is one of the major diseases affecting tomato production. Its early symptoms are often inconspicuous, yet the disease spreads rapidly, leading to severe economic losses. Therefore, the development of efficient and non-destructive early detection technologies is of critical importance. At present, multispectral imaging-based detection methods are constrained by two major bottlenecks: limited sample size and single modality, which hinder precise recognition at the early stage of infection. To address these challenges, this study explores a detection approach integrating multispectral fluorescence and reflectance imaging, combined with machine learning algorithms, to enhance early recognition of tomato gray mold. Particular emphasis is placed on evaluating the effectiveness of multimodal information fusion in extracting early disease features, and on elucidating the quantitative relationships between disease progression and key physiological indicators such as chlorophyll content, water content, malondialdehyde levels, and antioxidant enzyme activities. Furthermore, an improved WGAN-GP (Wasserstein Generative Adversarial Network with Gradient Penalty) is employed to alleviate data scarcity under small-sample conditions. The results demonstrate that multimodal data fusion significantly improves model sensitivity to early-stage disease detection, while WGAN-GP-based data augmentation effectively enhances learning performance with limited samples. The Random Forest model achieved an early recognition precision of 97.21% on augmented datasets, and transfer learning models attained an overall precision of 97.56% in classifying different disease stages. This study provides an effective approach for the early prediction of tomato gray mold, with potential application value in optimizing disease management strategies and reducing environmental impact. Full article

The burgeoning economic relationship between Türkiye and Poland, marked by a targeted $10 billion trade volume, has catalyzed significant Turkish engagement in the Polish construction sector. Ranked second globally in international contracting, Turkish firms are increasingly undertaking complex infrastructure projects in Poland, making it a critical European market to analyze. This study develops a comprehensive framework to identify and evaluate the sources of sustainable competitive advantage for Turkish contractors operating in this dynamic environment. The research adopts a qualitative, single-case study methodology, centered on the extensive project portfolio of a leading Turkish firm in Poland. The analytical approach is twofold. First, it employs Porter’s Diamond Framework to deconstruct the existing competitive advantages, revealing a shift from traditional low-cost models to a sophisticated synergy of superior labor management capabilities, strategic local partnerships, and expertise in complex project delivery. These strengths are shown to align directly with Poland’s critical needs, particularly its skilled labor shortage and ambitious infrastructure agenda. Second, a Foresight Analysis is conducted to map plausible future scenarios through 2035, addressing key uncertainties such as geopolitical shifts and the pace of technological adoption. The findings demonstrate that the sustained success of Turkish contractors hinges on their ability to deliver targeted value. The study concludes by proposing a set of “no-regrets” strategies—including accelerated ESG and digital up-skilling, forging deep local partnerships, and developing financial engineering capabilities—designed to secure and enhance their competitive positioning. The results provide an actionable roadmap for industry practitioners and valuable insights for policymakers fostering bilateral economic collaboration. Full article

Evaluation of socio-ecological vulnerability is crucial for sustainable management in mining cities. This study selected Xintai City, China, as a case and constructed a comprehensive vulnerability assessment framework based on 2010–2020 multi-source data. By integrating the Geodetector, spatial autocorrelation analysis, and ordered weighted averaging (OWA), we systematically explored the spatio-temporal patterns and driving mechanisms of socio-ecological vulnerability. The Theil index at the village level revealed finer spatial heterogeneity than large-scale analyses. The results show the following: (1) Socio-ecological vulnerability in Xintai City is generally moderate, with high-vulnerability areas concentrated in the urban center and former coal mining zones. Over the past decade, high—vulnerability levels in these areas have improved, whereas the urban-rural fringe has experienced a significant increase in vulnerability, primarily driven by industrial transfer and uneven resource allocation. (2) Geodetector analysis indicated a shift in dominant drivers from natural to socio-economic factors, with population density and construction land proportion surpassing natural conditions such as average annual rainfall by 2020. Additionally, mining land proportion, land use change, and the spatial distribution of social services played key roles in shaping vulnerability patterns, while ecological and public service factors showed weaker explanatory power. (3) Scenario simulation based on OWA demonstrated that an economic-priority pathway leads to the outward expansion of vulnerable areas into mountainous regions, while an ecological-priority approach promotes spatial contraction and optimization of vulnerability zones. These findings provide scientific guidance for identifying key vulnerable areas and formulating differentiated management strategies, offering reference value for the sustainable development of resource-exhausted mining cities. Full article

With the impact of fossil energy on the climate environment and the development of energy technologies, new energy vehicles, represented by electric cars, have begun to receive increasing attention and emphasis. The rapid proliferation of public charging infrastructure for NEVs has concurrently influenced traditional petrol station networks, creating measurable disparities in their spatial distributions that warrant systematic investigation. This research examines Wenzhou City, China, as a representative case area, employing multi-source Point of Interest (POI) data and spatial analysis models to analyse differential characteristics in spatial layout accessibility, service equity, and underlying driving mechanisms between public electric vehicle charging stations (EV) and traditional gas stations (GS). The findings reveal that public electric vehicle charging stations exhibit a pronounced “single-centre concentration with weak multi-centre linkage” spatial configuration, heavily reliant on dual-core drivers of population density and economic activity. This results in marked service accessibility declines in peripheral areas, resembling a cliff-like drop, and a relatively low spatial equity index. In contrast, traditional gas stations demonstrate a “core-axis linkage” diffusion pattern with strong coupling to urban road networks, showing gradient attenuation in service coverage efficiency along transportation arteries, fewer suburban service gaps, and more gradual accessibility reductions. Location entropy analysis further indicates that charging station deployment shows significant capital-oriented tendencies, with certain areas exhibiting paradoxical “excess facilities” phenomena, while gas station distribution aligns more closely with road network topology and transportation demand dynamics. Furthermore, the layout characteristics of public charging stations feature a more complex and diverse range of land use types, while traditional gas stations have a strong dependence on industrial land. This research elucidates the spatial distribution patterns of emerging and legacy energy infrastructure in the survey regions, providing critical empirical evidence for optimising energy infrastructure allocation and facilitating coordinated transportation system transitions. The findings also offer practical insights for the construction of energy supply facilities in urban development frameworks, holding substantial reference value for achieving sustainable urban spatial governance. Full article

To address the high fossil energy dependency and the low-value utilization of stillage (WDGS) in conventional cassava-based ethanol production—factors that increase greenhouse gas emissions and limit overall sustainability—this study develops an integrated ethanol–biogas–CHP system that valorizes stillage and enhances energy recovery. Three process scenarios were designed and evaluated through life cycle assessment (LCA) and techno-economic analysis: Case-I (WDGS dried and sold as animal feed), Case-II (stillage anaerobically digested for biogas used for heat), and Case-III (biogas further utilized in a combined heat and power system). Process simulation was conducted in Aspen Plus V11, while environmental impacts were quantified with the CML 2001 methodology under a cradle-to-gate boundary across six categories, including global warming potential (GWP) and abiotic depletion potential (ADP). Results show that Case-III achieves the highest environmental and economic performance, with a net GWP of −1515.05 kg CO₂-eq/ton ethanol and the greatest profit of 396.80 USD/ton of ethanol, attributed to internal energy self-sufficiency and surplus electricity generation. Sensitivity analysis further confirms Case-III’s robustness under variations in transportation distance and electricity demand. Overall, valorizing cassava stillage through biogas–CHP integration significantly improves the sustainability of ethanol production, offering a practical pathway toward low-carbon bioenergy with potential for negative emissions. This study fills a gap in previous life cycle research by jointly assessing WDGS utilization pathways with techno-economic evaluation, providing actionable insights for carbon-neutral bioenergy policies in cassava-producing regions. Certain limitations, such as software version and data accessibility, remain to be addressed in future work. Full article

Chrysanthemum morifolium is one of the world’s four major cut flowers, valued for its ornamental and economic importance. However, high temperature stress during growth and development can reduce both yield and quality. Autophagy is a cellular self-degradation and recycling process that plays a pivotal role in maintaining homeostasis under abiotic stress. This study aimed to identify autophagy-related genes (ATGs) in C. morifolium and its close relatives, analyze their structural and evolutionary characteristics, and evaluate ATG8 expression under heat stress. Genome-wide analysis identified 130 ATGs in C. morifolium, 51 in Chrysanthemum nankingense, and 49 in Chrysanthemum lavandulifolium. Genes within the same subfamily exhibited conserved structures and domains, with fragment duplication contributing to ATG expansion. Expression profiling showed that ATG8 genes were the most highly expressed and displayed tissue specificity, while heat stress induced their transcription, peaking at 48 h. These findings provide a comprehensive genomic resource for Chrysanthemum ATGs and indicate a potential role for ATG8 in heat stress responses, offering a basis for future studies aimed at improving thermotolerance in this ornamental crop. Full article

Accurate data on solar photovoltaic (PV) generation is essential for the effective prediction of energy production and the effective management of distributed energy resources (DERs). Such data also plays a crucial role in ensuring the operation of DERs within modern power distribution systems is both safe and economical. Missing values, which may be attributed to faults in sensors, communication failures or environmental disturbances, represent a significant challenge for distribution system operators (DSOs) in terms of performing state estimation, optimal dispatch, and voltage regulation. This paper proposes a Pattern-Aware Bidirectional Long Short-Term Memory (PA-BiLSTM) model for solar generation imputation to address this challenge. In contrast to conventional convolution-based approaches such as the Convolutional Autoencoder and U-Net, the proposed framework integrates a 1D convolutional module to capture local temporal patterns with a bidirectional recurrent architecture to model long-term dependencies. The model was evaluated in realistic block–random missing scenarios (1 h, 2 h, 3 h, and 4 h gaps) using 5 min resolution PV data from 50 sites across 11 regions in South Korea. The numerical results show that the PA-BiLSTM model consistently outperforms the baseline methods. For example, with a time gap of one hour, it achieves an MAE of 0.0123, an R² value of 0.98, and an average MSE, with a maximum reduction of around 15%, compared to baseline models. Even under 4 h gaps, the model maintains robust accuracy (MAE = 0.070, R² = 0.66). The results of this study provide robust evidence that accurate, pattern-aware imputation is a significant enabling technology for DER-centric distribution system operations, thereby ensuring more reliable grid monitoring and control. Full article