Search Results (1,413)

This review synthesizes the literature on the degradation and decomposition of holopelagic Sargassum, with a focus on process dynamics, including microbial contribution, process descriptions, and ecological impacts. Our objective is to consolidate a robust knowledge framework to inform and optimize management strategies in affected areas. Overall, we observed that the current literature relies primarily on isolated field ecological descriptions rather than a coherent, unified research line; mechanistic studies, including bacterial pathways and factors controlling degradation, remain scarce. At the fine scale, microbial community shifts during decomposition are strongly linked to the sequential utilization of distinct organic substrates, thereby favoring the proliferation of microorganisms capable of degrading complex organic molecules and of bacterial groups involved in sulfur respiration, methanogenesis, and nutrient recycling. In the case of sulfur respiration, groups such as Desulfobacterales and Desulfovibrionales may be responsible for the reported H₂S emissions, which pose significant public health concerns. At a broad scale, degradation occurs both on beaches during emersion and in the water column during immersion, particularly during massive accumulations. The initial stages are characterized by the release of organic exudates and leachates. Experimental and observational studies confirm a strong early-stage release of H₂S until the substrate is largely depleted. Depending on environmental conditions, a significant amount of biomass can be lost; however, this loss is highly variable, with notable consequences for contamination studies. Leachates may also contain low but ecologically significant amounts of arsenic, posing a potential contamination risk. Decomposition contributes to water-quality deterioration and oxygen depletion, with impacts at the individual, population, and ecosystem levels, yet many remain imprecisely attributed. Although evidence of nutrient enrichment in the water column is limited, studies indicate biological nutrient uptake. Achieving a comprehensive understanding of degradation and decomposition, including temporal and spatial dynamics, microbiome interactions, by means of directed research, is critical for effective coastal management, improved mitigation strategies, industrial valorization, and accurate modeling of biogeochemical cycles. Full article

Surface water is essential for sustaining ecosystems and supporting human socio-economic development, yet pollution from urbanization increasingly threatens its ecological sustainability. The accurate prediction of dissolved oxygen (DO), as an important indicator of water quality, is crucial for water resource protection. To address the methodological gaps in current research, we propose a hybrid deep learning model (GCG) that integrates spatiotemporal correlations to enhance DO prediction accuracy through the systematic exploitation of latent data dependencies. This study proposes a three-stage modeling framework: (1) A novel adjacency matrix construction methodology based on Pearson correlation coefficients is developed to quantify spatial correlations between monitoring stations, enabling spatial feature aggregation via graph convolutional networks (GCNs); (2) the spatially enhanced features are subsequently processed through 1D convolutional neural networks (CNNs) to capture temporal local patterns; (3) model performance is comprehensively evaluated using four metrics: R², RMSE, MAE, and MAPE. The proposed model was implemented for DO prediction in Lake Taihu, China. Experimental results demonstrate that compared to conventional adjacency matrix construction methods, the Pearson correlation-based adjacency matrix confers advantages, achieving at least a 5% reduction in RMSE and over 10% improvement in MAE and MAPE. Furthermore, the GCG model outperformed the comparison model, with an R² enhancement of 8%, while reducing RMSE and MAE by over 70% and 60%, respectively. These results validate the model’s effectiveness in mining spatiotemporal correlations for regional water quality forecasting, offering a reliable tool toward sustainable water monitoring and ecosystem-based management. Full article

A sharing framework based on Zero-Knowledge Proof (ZKP) and Proxy Re-encryption (PRE) technologies offers a promising solution for sharing Student Electronic Academic Records (SEARs). As core credentials in the education sector, student records are characterized by strong identity binding, the need for long-term retention, frequent cross-institutional verification, and sensitive information. Compared with electronic health records and government archives, they face more complex security, privacy protection, and storage scalability challenges during sharing. These records not only contain sensitive data such as personal identity and academic performance but also serve as crucial evidence in key scenarios such as further education, employment, and professional title evaluation. Leakage or tampering could have irreversible impacts on a student’s career development. Furthermore, traditional blockchain technology faces storage capacity limitations when storing massive academic records, and existing general electronic record sharing solutions struggle to meet the high-frequency verification demands of educational authorities, universities, and employers for academic data. This study proposes a dedicated sharing framework for students’ electronic academic records, leveraging PRE technology and the distributed ledger characteristics of blockchain to ensure transparency and immutability during sharing. By integrating the InterPlanetary File System (IPFS) with Ethereum Smart Contract (SC), it addresses blockchain storage bottlenecks, enabling secure storage and efficient sharing of academic records. Relying on optimized ZKP technology, it supports verifying the authenticity and integrity of records without revealing sensitive content. Furthermore, the introduction of gate circuit merging, constant folding techniques, Field-Programmable Gate Array (FPGA) hardware acceleration, and the efficient Bulletproofs algorithm alleviates the high computational complexity of ZKP, significantly reducing proof generation time. The experimental results demonstrate that the framework, while ensuring strong privacy protection, can meet the cross-scenario sharing needs of student records and significantly improve sharing efficiency and security. Therefore, this method exhibits superior security and performance in privacy-preserving scenarios. This framework can be applied to scenarios such as cross-institutional academic certification, employer background checks, and long-term management of academic records by educational authorities, providing secure and efficient technical support for the sharing of electronic academic credentials in the digital education ecosystem. Full article

The topic of Unmanned Aerial Vehicle (UAV) cybersecurity has received significant recent interest from the research community, with many methods proposed in the literature to improve detect and mitigate various types of attacks. This paper provides a comprehensive review of UAV cybersecurity, addressing all aspects of the UAV ecosystem and presenting a thorough review of the various types of UAV attacks, including a survey of recent real-world UAV cybersecurity incidents. UAV cybersecurity threat analysis and risk assessment methodologies are reviewed, discussing how potential attacks translate to UAV system risk. The various threat detection and countermeasure (mitigation) techniques are analyzed. Finally, this paper’s unique contribution is that it provides a survey of existing tools and datasets that are available to UAV cybersecurity researchers. A key identified research gap is the need to conduct real-world experimentation to validate proposed cybersecurity techniques. Many proposed approaches are computationally expensive or require additional redundant hardware onboard the UAV. Future research should focus on the development of lightweight methods that are practical for UAV adoption. Another key research gap is the relative lack of RemoteID cybersecurity research, despite its mandated adoption by UAVs. Lastly, this paper concludes that Global Positioning System (GPS)-related threats pose the greatest continued risk to UAVs. Full article

To screen efficient microbial coating formulations and explore their effects on the growth of alfalfa and soil properties, ‘Gannong No. 3’ alfalfa was used as the experimental material. A single-factor randomized block field experiment was conducted with eight treatments (CK as bare seeds, BC as adhesive filler coated agent, J1-J3 as rhizobium agent, growth-promoting bacteria agent, and rhizobium plus growth-promoting bacteria seed soaking, respectively, B1-B3 as rhizobium, growth-promoting bacteria, and rhizobium plus growth-promoting bacteria coating agents, respectively). This study analyzes the effects of different microbial coating formulations on alfalfa, including its production performance and nutritional quality, as well as on soil properties. Comprehensive analysis shows that the growth-promoting microbial coating (B2) is the optimal formulation. It can simultaneously optimize alfalfa production performance, enhance nutritional quality, improve soil particle composition, and increase soil enzyme activity, achieving a synergistic improvement of both alfalfa and the soil ecosystem. Its application effect is significantly better than other treatments and can provide important theoretical support and practical reference for the development and application of efficient microbial seed coatings in high-quality alfalfa cultivation. Full article

Microplastics are among the most emerging environmental micro-threats to aquatic ecosystems. Bivalves are filter-feeding benthic organisms and are often considered excellent bioindicators of contamination in aquatic bodies. This study focuses on the toxic effects of fibrous polystyrene microplastics (1 mg/L) on biochemical parameters of the freshwater bivalve Lamellidens marginalis after exposure periods of 7, 10, and 15 days (Experimental groups I, II, and III, respectively). Biochemical analysis showed reduced protein, ACP, and ALP activities in all tissues except for a significant increase in ACP in the mantle and foot of group III. AST activity increased in the gill and hepatopancreas but declined in the mantle and foot. ALT activity consistently decreased across all experimental tissues relative to the control. The Integrated Biomarker Response Index increased over time for gill, mantle, and foot tissue. For the hepatopancreas, the values were 11, 8.82, and 9.02 for Experimental groups I, II, and III, respectively. From Biomarker Response Index values, group I gill tissue (2.2) was most severely altered. Major alterations occurred in the hepatopancreas, mantle, and foot of groups II and III. Hepatopancreas generally acts as a site of detoxification, digestion, and absorption, and exposure to microplastics can lead to the accumulation in hepatopancreas. Full article

Microbial communities associated with animal cadaver decomposition play a crucial role in biogeochemical cycles in both aquatic and terrestrial ecosystems. However, it remains unclear regarding the diversity, succession, and assembly of phosphate-solubilizing microbes during animal cadaver decay. In this study, plateau pikas (Ochotona curzoniae) as mammal degradation models were placed on alpine meadow soils to study diversity, succession and assembly of phosphate-solubilizing microbes using amplicon sequencing of phoC- and phoD-genes during 94 days of incubation. The total phosphorus concentration in the corpse group increased by 8.53% on average. Alpha diversity of both phoC- and phoD-harboring microbes decreased in the experimental group compared to the control group, and the community structure differed between control and experimental groups. Phosphate-solubilizing microbial community turnover time rate (TDR) of the experimental group was higher than that of the control group, indicating corpse decay accelerates the succession of phoC- and phoD-harboring microbial community. Null model revealed that deterministic process dominated phoC microbial community in corpse group, while the stochastic process dominated phoD microbial community. The microbial network in experimental group was more complicated than that in control group of phoC microbial community, while phoD microbial community showed opposite trend. Partial least squares path modeling (PLS-PM) showed that phoC-harboring microbial community was mainly influenced by pH, Total carbon (TC) and Total phosphorus (TP), while the phoD microbial community was only regulated by TP. These findings elucidate the ecological mechanism of phosphorus-solubilizing microbial community changes during animal corpse degradation. Full article

Problem: Entrepreneurship education continues to expand, yet it remains fragmented across disciplines and loosely connected to the knowledge, innovation, and venture ecosystems that shape entrepreneurial success. At the same time, AI is transforming research, collaboration, and venture development, but its use in education is typically limited to narrow, task-specific applications rather than ecosystem-level integration. Objective: This paper seeks to develop a comprehensive conceptual model for integrating AI into entrepreneurship education by positioning AI as a connective infrastructure that links and activates the knowledge, innovation, and venture ecosystems. Methods: The model is derived through an integrative synthesis of literature, programs, and activities on entrepreneurship education, ecosystem-based learning, and AI-enabled research and innovation practices, combined with an analysis of gaps in current educational approaches. Key Findings: The proposed model defines a progressive learning pathway consisting of (1) AI competency training that builds foundational capacities in critical judgment, responsible application, and creative adaptation; (2) AI praxis labs that use AI-curated ecosystem data to support iterative, project-based learning; and (3) venture studios where students scale outputs into innovations and ventures through structured ecosystem engagement. This pathway demonstrates how AI can function as a structural mediator of problem definition, research design, experimentation, analysis, and narrative translation. Contributions: This paper reframes entrepreneurship education as an iterative, inclusive, and ecosystem-connected process enabled by AI infrastructure. It offers a new theoretical lens for understanding AI’s educational role and provides actionable implications for curriculum design, institutional readiness, and policy development while identifying avenues for future research on competency development and ecosystem impacts. Full article

Industry 5.0 challenges higher education to adopt human-centred and sustainable uses of artificial intelligence, yet many current deployments still treat generative AI as a stand-alone tool, neurophysiological sensing as largely laboratory-bound, and governance as an external add-on rather than a design constraint. This article introduces Nested Learning as a neuro-adaptive ecosystem design in which generative-AI agents, IoT infrastructures and multimodal deep learning orchestrate instructional support while preserving student agency and a “pedagogy of hope”. We report an exploratory two-phase mixed-methods study as an initial empirical illustration. First, a neuro-experimental calibration with 18 undergraduate students used mobile EEG while they interacted with ChatGPT in problem-solving tasks structured as challenge–support–reflection micro-cycles. Second, a field implementation at a university in Madrid involved 380 participants (300 students and 80 lecturers), embedding the Nested Learning ecosystem into regular courses. Data sources included EEG (P300) signals, interaction logs, self-report measures of engagement, self-regulated learning and cognitive safety (with strong internal consistency; $\alpha /\omega \ge 0.82$), and open-ended responses capturing emotional experience and ethical concerns. In Phase 1, P300 dynamics aligned with key instructional micro-events, providing feasibility evidence that low-cost neuro-adaptive pipelines can be sensitive to pedagogical flow in ecologically relevant tasks. In Phase 2, participants reported high levels of perceived nested support and cognitive safety, and observed associations between perceived Nested Learning, perceived neuro-adaptive adjustments, engagement and self-regulation were moderate to strong ($r=0.41$–$0.63$, $p<0.001$). Qualitative data converged on themes of clarity, adaptive support and non-punitive error culture, alongside recurring concerns about privacy and cognitive sovereignty. We argue that, under robust ethical, data-protection and sustainability-by-design constraints, Nested Learning can strengthen academic resilience, learner autonomy and human-centred uses of AI in higher education. Full article

Between 2018 and 2025, alternative finance expanded while micro-, small- and medium-sized enterprises in emerging economies continued to face a substantial funding gap. This study examines how entrepreneurial frugality articulates frugal ecosystems, access to alternative finance, resilience and SME performance within a single explanatory framework. Following PRISMA 2020 and PRISMA-S, we conduct a systematic review of Scopus, Web of Science and Cairn; out of 1483 records, 106 peer-reviewed studies are retained and assessed using the Mixed Methods Appraisal Tool and a narrative synthesis approach. The findings show that frugal ecosystems characterized by pooled assets, norms of repair and modularity, and lightweight digital tools reduce experimentation costs and develop frugal innovation as an organizational capability. This capability enhances access to alternative finance by generating readable quality signals, while non-bank channels provide a financial buffer that aligns liquidity with operating cycles and strengthens entrepreneurial resilience. The article proposes an operationalized conceptual model, measurement guidelines for future quantitative surveys, and public policy and managerial implications to support frugal and inclusive innovation trajectories in emerging contexts. Full article

Zero-Touch Network (ZTN) represents a cornerstone approach of Next Generation Networks (NGNs), enabling fully automated and AI-driven network and service management. However, their distributed and multi-domain nature introduces critical security challenges, particularly regarding service identity and data integrity. This paper proposes a novel blockchain-based framework to enhance the security of ZTN through two complementary mechanisms: decentralized digital identity management and oracle-assisted network monitoring. First, a Decentralized Identity Management framework aligned with Zero-Trust Architecture principles is introduced to ensure tamper-proof authentication and authorization in a trustless environment among network components. By leveraging decentralized identifiers, verifiable credentials, and zero-knowledge proofs, the proposed Decentralized Authentication and Authorization component eliminates reliance on centralized authorities, while preserving privacy and interoperability across domains. Second, the paper investigates blockchain oracle mechanisms as a means to extend data integrity guarantees beyond the blockchain, enabling secure monitoring of Network Services and validation of Service-Level Agreements. We propose a four-dimensional framework for oracle design, based on qualitative comparison of oracle types—decentralized, compute-enabled, and consensus-based—to identify their suitability for NGN scenarios. This work proposes an architectural and design framework for Zero-Touch Networks, focusing on system integration and security-aware orchestration rather than large-scale experimental evaluation. The outcome of our study highlights the potential of integrating blockchain-based identity and oracle solutions to achieve resilient, transparent, and self-managed network ecosystems. This research bridges the gap between theory and implementation by offering a holistic approach that unifies identity security and data integrity in ZTNs, paving the way towards trustworthy and autonomous 6G infrastructures. Full article

Ecological flow management is important for maintaining ecosystem stability and promoting sustainable development. Dynamic ecological flow regulation depends on precise real-time monitoring of water levels and flow velocities. To address challenges in ecological flow monitoring, including maintenance difficulties and insufficient accuracy, an improved DSC-YOLOv8n-seg model is proposed for dynamic multi-parameter sensing, achieving more efficient object detection and semantic segmentation. Compared with traditional affine transformation-edge detection, this approach enables joint recognition of water level lines and staff gauge characters, achieving an average recognition error of ±1.2 cm, with a model accuracy of 93.1%, recall rate of 94.5%, and mAP50:95 of 93.9%. A deep learning-based spectral principal direction recognition method was also employed to calculate the surface water flow velocity, which demonstrated stable and efficient performance, achieving a relative error of 0.005 m/s for the surface velocity. Experimental results confirm that it can effectively address issues such as environmental interference, exhibiting enhanced robustness in low-light and nighttime scenarios. The proposed method provides efficient and accurate identification for dynamic water level monitoring and for real-time detection of river surface flow velocities to improve ecological flow management. Full article

Nitrogen (N) deposition poses a multi-pronged threat to the carbon (C)-regulating services of moss understories. For forest C-cycle modeling under increasing N deposition, failure to mechanistically incorporate the moss-mediated processes risks severely overestimating the C sink potential of global forests. To explore whether and how N input affects the moss-mediated CH₄ and carbon dioxide (CO₂) fluxes, a five-year field measurement was performed in the N manipulation experimental plots treated with 22.5 and 45 kg N ha⁻¹ yr⁻¹ as ammonium chloride for nine years under a well-drained temperate forest in northeastern China. In the presence of mosses, the average annual CH₄ uptake and CO₂ emission in all N-treated plots ranged from 0.96 to 1.48 kg C-CH₄ ha⁻¹ yr⁻¹ and from 4.04 to 4.41 Mg C-CO₂ ha⁻¹ yr⁻¹, respectively, with a minimum in the high-N-treated plots, which were smaller than those in the control (1.29–1.83 kg C-CH₄ ha⁻¹ yr⁻¹ and 4.82–6.51 Mg C-CO₂ ha⁻¹ yr⁻¹). However, no significant differences in annual cumulative CO₂ and CH₄ fluxes across all treatments occurred without moss cover. Based on the differences in C fluxes with and without mosses, the average annual moss-mediated CH₄ uptake and CO₂ emission in the control were 0.77 kg C-CH₄ ha⁻¹ yr⁻¹ and 2.40 Mg C-CO₂ ha⁻¹ yr⁻¹, respectively, which were larger than those in the two N treatments. The N effects on annual moss-mediated C fluxes varied with annual meteorological conditions. Soil pH, available N and C contents, and microbial activity inferred from δ¹³C shifts in respired CO₂ were identified as the main driving factors controlling the moss-mediated CH₄ and CO₂ fluxes. The results highlighted that this inhibitory effect of increasing N deposition on moss-mediated C fluxes in the context of climate change should be reasonably taken into account in model studies to accurately predict C fluxes under well-drained forest ecosystems. Full article

Our work develops the structural mathematical framework of the REGENerative Agriculture (REGENA) Production Function, contributing to the limited global literature of regenerative farming production functions with consistency to the 2nd Law of Thermodynamics and the underlying biophysical processes for ecosystem services’ generation. The accurate structural economic modeling of regenerative farming practices comprises a first vital step for the shift of global agriculture from conventional farming—utilizing petrochemical fertilizers, pesticides and intensive tillage—to regenerative farming—utilizing local agro-ecological capital forms, such as micro-organisms, organic biomasses, no-tillage and resistant varieties. In this context, we empirically test the REGENA structural change patterns with data from eight experimental plots in six Mediterranean countries in Southern Europe and Northern Africa for three crop compositions: (a) with exclusively conventional practices, (b) with exclusively regenerative practices and (c) with mixed conventional and regenerative practices. Finally, we discuss in detail the scientific, institutional, economic and financial engineering challenges for the market uptake of regenerative farming and the contribution of REGENA for the achievement of this goal. In addition, as regenerative farming is knowledge-intensive, we review the vital aspect of Open Innovation (OI) and protected Intellectual Property (IP) business models as essential parts of regenerative farming knowledge-sharing clusters and trading alliances. Full article

Drought severely limits plant growth, threatening global food security and biodiversity. This review provides a comprehensive overview of the recent advances in plant responses to drought, ranging from initial sensing to physiological adaptation, as well as guidelines for experimental design. We focus on key regulatory components, specifically the ABA signaling core (PYR/PYL/RCARs, PP2C phosphatases, and SnRK2 kinases) and ROS signaling. We provide a detailed description of transcriptional networks, highlighting the pivotal roles of DREB, NAC, and MYB transcription factors in coordinating gene expression. Furthermore, we explore downstream tolerance strategies, including osmoprotectant (e.g., proline) accumulation, cell wall remodeling involving expansins and pectin methylesterases, as well as stomatal regulation. We also discuss how combining genetics with multi-omics and high-throughput phenotyping bridges the gap between molecular mechanisms and whole-plant physiological performance. Ultimately, these insights provide a foundation for refining research approaches and accelerating the development of drought-resilient crops to sustain agricultural productivity and ecosystem stability in increasingly arid environments. Full article