Search Results (1,157)

This study investigated the impact of green tea addition on microbial community dynamics during Daqu fermentation, a critical process in traditional baijiu production. Four Daqu variants (0%, 10%, 20%, 30% tea) were analyzed across six fermentation periods using 16S rRNA/ITS sequencing, coupled with STR, TDR, Sloan neutral model, and phylogenetic analyses. Results showed time-dependent increases in bacterial/fungal richness, with 30% tea maximizing species richness. Tea delayed bacterial shifts until day 15 but accelerated fungal reconstruction from day 6, expanding the temporal response window. While stochastic processes dominated initial assembly (77–94% bacteria, 88–99% fungi), deterministic processes intensified with tea concentration, particularly in fungi (1% → 12%). Tea increased bacterial dispersal limitation and reduced phylogenetic conservatism of endogenous factors. This work proposed a framework for rationally engineering fermentation ecosystems by decoding evolutionary-ecological rules of microbial assembly. It revealed how plant-derived additives can strategically adjust niche partitioning and ancestral constraints to reprogram microbiome functionality. These findings provided a theoretical foundation in practical strategies for optimizing industrial baijiu production through targeted ecological interventions. Full article

As a critical ecological security node in North China, the Baiyangdian Basin underpins regional water resources, biodiversity conservation, and environmental risk mitigation. Its ecological integrity is fundamental to the sustainable development of the Beijing–Tianjin–Hebei (BTH) megaregion. This study leveraged Google Earth Engine (GEE) to quantify spatiotemporal ecosystem dynamics within the Baiyangdian watershed from 1990 to 2023, utilizing the Remote Sensing Ecological Index (RSEI). The primary drivers influencing the watershed’s ecological and environmental quality were subsequently analyzed. The results show that the ecological quality of the Baiyangdian Basin showed fluctuating changes from 1990 to 2023. Overall, the northwestern part of the Baiyangdian Basin improved significantly, while the southeastern part was slightly degraded, and the intensity of the change between different RSEI grades was low, mainly fluctuating between poor, medium, and good grades. Both anthropogenic and natural factors have high explanatory power for the ecological quality of the Baiyangdian watershed, and the land use type in particular is the main driver of changes in the RSEI area. The explanatory power of these factors was significantly enhanced by the interaction between them, especially the interaction between the land use type and other drivers. Within the drivers of the land use type, the cropland area, woodland area, shrub area, and grassland area have a significant influence. In summary, the area change in different land use types is the main factor influencing the ecological quality of the Baiyangdian watershed. This study has demonstrative value and implications for large-scale shallow lakes and wetlands, ecological barriers in rapidly urbanizing regions, the integrated management of cross-administrative watersheds, and the use of the GEE platform for long time-series and large-scale ecological monitoring and assessment. Full article

Small streams often underwent engineering works conducted without special care for the hydromorphological and ecosystemic consequences. To allow small Walloon watercourses to comply with the European Water Framework Directive, renaturation is required. However, the cost of such projects is often prohibitive for small streams. Therefore, the Rivialis project aims at designing a methodology to support such works, based on a “small river quality index” that requires the collection of various data under the form of an “ID card” of the investigated river reach, allowing to obtain a synthetic overview of the key features of the study reach. Such an ID card, and ultimately the index, should include the most relevant components among existing morphological and biological indicators. To reduce the project costs, the number of field measurements to build this ID card should be limited; the data should be obtained from online and publicly available data sources or easily collected on site. In this paper, key metrics are identified from the literature. They are then determined along a reach of the Petit Bocq River with the aim of assessing those that can be obtained at a low cost from available databases and those that require more costly field investigations. The results show that combining available databases and numerical simulations allows determining a river reach ID card yielding a first set of valuable information at a low cost. Field surveys can then be limited to the verification of these values and to the collection of biological information. Full article

The integration of electric vehicles (EVs) into smart grids (SGs) is reshaping both energy systems and mobility infrastructures. This review presents a comprehensive and cross-disciplinary synthesis of current technologies, methodologies, and challenges associated with EV–SG interaction. Unlike prior reviews that address these aspects in isolation, this work uniquely connects three critical pillars: (i) the evolution of energy storage technologies, including lithium-ion, second-life, and hybrid systems; (ii) optimisation and predictive control techniques using artificial intelligence (AI) for real-time energy management and vehicle-to-grid (V2G) coordination; and (iii) cybersecurity risks and post-quantum solutions required to safeguard increasingly decentralised and data-intensive grid environments. The novelty of this review lies in its integrated perspective, highlighting how emerging innovations, such as federated AI models, blockchain-secured V2G transactions, digital twin simulations, and quantum-safe cryptography, are converging to overcome existing limitations in scalability, resilience, and interoperability. Furthermore, we identify underexplored research gaps, such as standardisation of bidirectional communication protocols, regulatory inertia in V2G market participation, and the lack of unified privacy-preserving data architectures. By mapping current advancements and outlining a strategic research roadmap, this article provides a forward-looking foundation for the development of secure, flexible, and grid-responsive EV ecosystems. The findings support policymakers, engineers, and researchers in advancing the technical and regulatory landscape necessary to scale EV–SG integration within sustainable smart cities. Full article

Urban flooding presents increasingly complex challenges exacerbated by climate change, rapid urbanization, and aging infrastructure. This investigation combines planning theories and socio-hydrological modelling to create a planning-adaptable urban flood management strategy. The case study of Chiba Prefecture, Japan, demonstrates this approach in depth. By applying the Social-Ecological-Technological Systems (SETS) framework in combination with planning theories, the study has identified the relationship between the conventional engineered methods and the newly introduced environmentally friendly (nature-based) solutions. Our findings, which are based on content analysis of 23 official statutory planning documents, indicate that there is a significant focus on the conservation of ecosystems and green infrastructure balanced with issues of emergency planning and community engagement. One of the points that the results highlight is integrating the ecological, social and technological aspects in order to create flood management policies that are both robust and fair. This integrated approach offers a robust framework for mitigating flood risks while promoting sustainable urban development and long-term community resilience. Full article

Accurate crop mapping is vital for monitoring agricultural resources, food security, and ecosystem sustainability. Advances in high-resolution sensing technologies now enable precise, large-scale crop mapping, improving agricultural management and decision-making. However, in scenarios where balancing precision and computational resources is important, obtaining the optimal feature combination (especially newly proposed features) and strategies from the rich feature sets contained in multi-source remote sensing imagery remains one of the challenges. In this paper, we propose a hierarchical feature optimization method, incorporating a newly reported vegetation feature, for mapping crop types by combining the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery within the Google Earth Engine (GEE) platform. The method first calculates spectral features, texture features, polarization features, vegetation index features, and crop phenological features, with a particular focus on infrared band features and the newly developed Kernel Normalized Difference Vegetation Index (kNDVI). These 126 features are then selected to construct 15 crop type mapping models based on different feature combinations and a random forest (RF) classifier. Feature selection was performed using the feature correlation analysis and random forest recursive feature elimination (RF-RFE) to identify the optimal subset. The experiment was conducted in the Linhe region, covering an area of 2333 km². The resulting 10 m crop map, generated by the optimal model (Model 15) with 34 key features, demonstrated that integrating multi-source features significantly enhances mapping accuracy. The model achieved an overall accuracy of 90.10% across five crop types (corn, wheat, sunflower, soybean, and beet), outperforming other representative feature optimization methods, Relief-F (87.50%) and CFS (89.60%). The study underscores the importance of feature optimization and reduction of redundant features while also showcasing the effectiveness of red edge and infrared features, as well as the kNDVI, in mapping crop type. Full article

In the rapidly evolving landscape of digital marketing and electronic commerce, short-form content—particularly on platforms like Twitter (now X)—has become pivotal for real-time branding, community engagement, and product promotion. The rise of Non-Fungible Tokens (NFTs) and Web3 ecosystems further underscores the need for domain-specific, engagement-oriented social media content. However, automating the generation of such content while balancing linguistic quality, semantic relevance, and audience engagement remains a substantial challenge. To address this, we propose RL-TweetGen, a socio-technical framework that integrates instruction-tuned large language models (LLMs) with reinforcement learning (RL) to generate concise, impactful, and engagement-optimized tweets. The framework incorporates a structured pipeline comprising domain-specific data curation, semantic classification, and intent-aware prompt engineering, and leverages Parameter-Efficient Fine-Tuning (PEFT) with LoRA for scalable model adaptation. We fine-tuned and evaluated three LLMs—LLaMA-3.1-8B, Mistral-7B Instruct, and DeepSeek 7B Chat—guided by a hybrid reward function that blends XGBoost-predicted engagement scores with expert-in-the-loop feedback. To enhance lexical diversity and contextual alignment, we implemented advanced decoding strategies, including Tailored Beam Search, Enhanced Top-p Sampling, and Contextual Temperature Scaling. A case study focused on NFT-related tweet generation demonstrated the practical effectiveness of RL-TweetGen. Experimental results showed that Mistral-7B achieved the highest lexical fluency (BLEU: 0.2285), LLaMA-3.1 exhibited superior semantic precision (BERT-F1: 0.8155), while DeepSeek 7B provided balanced performance. Overall, RL-TweetGen presents a scalable and adaptive solution for marketers, content strategists, and Web3 platforms seeking to automate and optimize social media engagement. The framework advances the role of generative AI in digital commerce by aligning content generation with platform dynamics, user preferences, and marketing goals. Full article

The swift growth of the low-altitude economic ecosystem and Unmanned Aerial Vehicle (UAV) swarm applications across diverse sectors presents significant challenges for engineering managers in terms of effective performance evaluation and operational optimization. Traditional evaluation methods often struggle with the inherent complexities, dynamic nature, and multi-faceted performance criteria of UAV swarms. This study introduces a novel Bayesian Network (BN)-based multicriteria decision-making framework that systematically integrates expert intuition with real-time data. By employing variance decomposition, the framework establishes theoretically grounded, bidirectional mapping between expert-assigned weights and the network’s probabilistic parameters, creating a unified model of subjective expertise and objective data. Comprehensive validation demonstrates the framework’s efficacy in identifying critical performance drivers, including environmental awareness, communication ability, and a collaborative decision. Ultimately, our work provides engineering managers with a transparent and adaptive tool, offering actionable insights to inform resource allocation, guide technology adoption, and enhance the overall operational effectiveness of complex UAV swarm systems. Full article

Controlled thermal stratification in water is crucial for applications such as testing the thermal stealth of underwater vehicles and studying aquatic ecosystems. However, existing laboratory methods for generating such stratified environments often lack stability and uniformity. This study systematically investigates the influence of various hot water injection methods on the stability of thermal stratification. A computational fluid dynamics model, incorporating the overlapping dynamic mesh technique and the Volume of Fluid free-surface capturing method, was used to compare four generation strategies: single-side fixed discharge, towed horizontal discharge, towed vertical upward discharge, and multi-nozzle towed vertical upward discharge. The results indicate that towed discharge methods produce more stable and uniform thermal stratification compared to the fixed discharge method, achieving a 10.1% increase in the water body’s vertical stability coefficient and a 4.5% increase in the Richardson number, while the maximum surface temperature difference was significantly reduced from 0.98 K to 0.37 K. Among the towed methods, vertical upward discharge demonstrated superior stability over horizontal discharge, as it directly transports the high-temperature plume to the upper layer, minimizing thermal exchange with the lower layer, with its vertical stability coefficient and Richardson number being 17.9% and 23% higher, respectively. While maintaining a constant heat input per unit volume, the multi-nozzle configuration yielded ${\mathrm{N}}^2$ and Ri values comparable to the single-nozzle version, while further improving the temperature uniformity at the free surface. Consequently, the towed vertical upward discharge emerges as a highly efficient method for establishing stable and uniform thermal stratification, with the multi-nozzle configuration showing significant promise for large-scale applications. This study provides a valuable reference for creating stratified fluid environments and for related engineering fields. Full article

Forest disturbance is a major driver shaping the structure and function of plantation ecosystems. Current research predominantly focuses on single forest types or landscape scales. However, species-level fine-scale assessments of disturbance dynamics are still scarce. In this study, we investigated Chinese fir (Cunninghamia lanceolata), Armand pine (Pinus armandii), and Yunnan pine (Pinus yunnanensis) plantations in the mountainous eastern Yunnan Plateau. We developed a Spatial Coupling Framework of Disturbance Legacy (SC-DL) to systematically elucidate the spatial associations between contemporary species distribution patterns and historical disturbance regimes. Using the Google Earth Engine (GEE) platform, we reconstructed pixel-level disturbance trajectories by integrating long-term Landsat time series (1993–2024) and applying the LandTrendr algorithm. By fusing multi-source remote sensing features (Sentinel-1/2) with terrain factors, employing RFE, and performing a multi-model comparison, we generated 10 m-resolution species distribution maps for 2024. Spatial overlay analysis quantified the cumulative proportion of the historically disturbed area and the spatial aggregation patterns of historical disturbances within current species ranges. Key results include the following: (1) The model predicting disturbance year achieved high accuracy (R² = 0.95, RMSE = 2.02 years, MAE = 1.15 years). The total disturbed area from 1993 to 2024 was 872.7 km², exhibiting three distinct phases. (2) The random forest (RF) model outperformed other classifiers, achieving an overall accuracy (OA) of 95.17% and a Kappa coefficient (K) of 0.93. Elevation was identified as the most discriminative feature. (3) Significant spatial differentiation in disturbance types emerged: anthropogenic disturbances (e.g., logging and reforestation/afforestation) dominated (63.1% of total disturbed area), primarily concentrated within Chinese fir zones (constituting 70.2% of disturbances within this species’ range). Natural disturbances accounted for 36.9% of the total, with fire dominating within the Yunnan pine range (79.3% of natural disturbances in this zone) and drought prevailing in the Armand pine range (71.3% of natural disturbances in this zone). (4) Cumulative disturbance characteristics differed markedly among species zones: Chinese fir zones exhibited the highest cumulative proportion of disturbed area (42.6%), with strong spatial aggregation. Yunnan pine zones followed (36.5%), exhibiting disturbances linearly distributed along dry–hot valleys. Armand pine zones showed the lowest proportion (20.9%), characterized by sparse disturbances within fragmented, high-altitude habitats. These spatial patterns reflect the combined controls of topographic adaptation, management intensity, and environmental stress. Our findings establish a scientific basis for identifying disturbance-prone areas and inform the development of differentiated precision management strategies for plantations. Full article

The rapid development of the Internet of Things (IoT) has enabled the implementation of interconnected intelligent systems in extremely dynamic contexts with limited resources. However, traditional paradigms, such as those using ECC-based heuristics and centralised decision-making frameworks, cannot be modernised to ensure resilience, scalability and security while taking quantum threats into account. In this case, we propose a modular architecture that integrates quantum-inspired cryptography (QI), epistemic uncertainty reasoning, the multiscale blockchain MuReQua, and the quantum-inspired decentralised storage engine (DeSSE) with fragmented entropy storage. Each component addresses specific cybersecurity weaknesses of IoT devices: quantum-resistant communication on epistemic agents that facilitate cognitive decision-making under uncertainty, lightweight adaptive consensus provided by MuReQua, and fragmented entropy storage provided by DeSSE. Tested through simulations and use case analyses in industrial, healthcare and automotive networks, the architecture shows exceptional latency, decision accuracy and fault tolerance compared to conventional solutions. Furthermore, its modular nature allows for incremental integration and domain-specific customisation. By adding reasoning, trust and quantum security, it is possible to design intelligent decentralised architectures for resilient IoT ecosystems, thereby strengthening system defences alongside architectures. In turn, this work offers a specific architectural response and a broader perspective on secure decentralised computing, even for the imminent advent of quantum computers. Full article

Petroleum-contaminated terrestrial ecosystems require effective bioremediation strategies. In this study, genomic analysis revealed key biodegradation genes on the Acinetobacter calcoaceticus 21# chromosome: alkane hydroxylases (alkB, almA, LadA) and aromatic ortho-cleavage pathway genes (catABC). Phylogenetic and multiple sequence alignment analyses of the almA gene in strain 21# revealed the presence of signature motifs characteristic of Baeyer–Villiger monooxygenase. Functional annotation analysis demonstrated stronger phylogenetic affinity of this protein to previously characterized BVMOs than to hydroxylases. Therefore, it is suggested that the AlmA protein in 21# exhibits BVMO activity and participates in the subterminal oxidation pathway of alkane degradation. Wild-type 21# degraded both n-Octacosane (24.47%) and pyrene (34.03%). Engineered 21#-A3 showed significantly enhanced n-Octacosane degradation (28.68%). To validate AlmA function and assess impacts of exogenous gene integration, we expressed the almA gene from A. vivianii KJ-1 via pET-28a(+)-av-almA-BH vector. Enzymatic assays demonstrated no activity toward long-chain alkanes but high activity for 2-decanone (0.39 U/mg) and 2-dodecanone (0.37 U/mg). Metabolite analysis confirmed recombinant AlmA functions through subterminal oxidation. This study establishes a foundational framework for advancing the optimization of petroleum-degrading bacteria. To engineer more efficient hydrocarbon-degrading strains, future research should integrate meta-cleavage pathways to expand their substrate utilization range for polycyclic aromatic hydrocarbons. Full article

China, facing severe saline–alkali land degradation, is grappling with the paradox of technically adequate but systemically deficient land consolidation. In response to the existing evaluation system’s over-reliance on physicochemical indicators and neglect of socioeconomic value, this study proposes the use of the Optimal Land Use Value (OLV) to construct a comprehensive benefit evaluation indicator system for saline–alkali land consolidation that encompasses ecosystem resilience, supply–demand balancing, and common prosperity. Considering a case project implemented from 2019 to 2022 in the Western Songnen Plain of China—one of the world’s most severely affected soda saline–alkali regions—this study combines the land use transition matrix with a comprehensive evaluation model to systematically assess the effectiveness and sustainability of land consolidation. The results reveal systemic deficiencies: within ecological spaces, short-term desalination succeeds but pH and organic matter improvements remain inadequate, while ecosystem vulnerability increases due to climate fluctuations and grassland conversion. In production spaces, cropland expansion and saline land reduction are effective, but water resource management proves unsustainable. Living spaces show improved infrastructure and income but face threats due to economic simplification and intergenerational unsustainability. For the investigated case, recommendations include shifting from technical restoration to systemic governance via three strategies: (1) biological–engineering synergy employing green manure to enhance soil microbial activity; (2) hydrological balancing through groundwater quotas and rainwater utilization; (3) specialty industry development for rural economic diversification. This study contributes empirical evidence on the conversion of saline–alkali land, as well as an evaluation framework of wider relevance for developing countries combating land degradation and pursuing rural revitalization. Full article

Digital transformation is gradually emerging as a core driver of high-quality economic development and the achievement of sustainable development goals. As the core engine of the digitalization process, digital innovation is becoming a key supporting force for activating the value of digital elements and fostering a new development pattern. Based on panel data from 31 provincial-level administrative regions in China spanning from 2019 to 2023, we undertook an in-depth exploration of how information ecosystems and digital transformation drivers synergistically enhance regional digital innovation capabilities and their substitutive relationships. The results show the following: Firstly, technological, subject, information, and environmental conditions cannot individually constitute the necessary conditions for a high level of regional digital innovation capacity. Secondly, three types of conditional configurations, namely technology–environment-driven, subject–environment-driven, and balance-driven, are its main driving paths, with significant substitution effects among these conditions. This study provides theoretical and empirical evidence for formulating region-specific strategies to optimize digital innovation capacity and helps formulate differentiated digital development strategies based on regional resource endowments and institutional characteristics, ultimately promoting the construction of a more balanced and resilient digital innovation ecosystem. Full article

Urban expressway interchanges, though primarily engineered for traffic efficiency, also serve as crucial ecological nodes within urban landscapes. This study evaluates the ecological functions of arborous vegetation across four typical interchange configurations—cloverleaf, single trumpet, double trumpet, and irregular—along the Nanjing Ring Expressway. Using the i-Tree Eco model, we quantified key ecosystem services, including carbon sequestration and storage, air pollutant removal, and stormwater mitigation. Field surveys documented 7985 trees from 45 species, with the 10 most abundant accounting for over two-thirds of total individuals. Results revealed that the trees sequester around 115 tons of carbon annually and store nearly 1850 tons in total, equivalent to an estimated economic benefit of ¥5.8 million. Trees also removed more than 1.5 tons of air pollutants and intercepted nearly 2400 cubic meters of stormwater each year. Species such as Sophora japonica, Phoebe zhennan, and Cinnamomum camphora emerged as key contributors to ecological performance. Among interchange types, double trumpet configurations yielded the highest overall service value, while single trumpet interchanges demonstrated superior efficiency per unit area. These findings highlight the underutilized ecological potential of transport-adjacent green spaces and underscore the importance of species selection and spatial design in maximizing multifunctional benefits. Full article