Search Results (153)

Aquatic environments that support tourism, including coasts, lakes, reservoirs, and estuaries, are experiencing accelerating eutrophication worldwide. This trend increases the frequency and intensity of algal blooms. These blooms undermine ecosystem services and weaken the socio-economic performance of destination areas. Despite these challenges, existing research remains fragmented. Aquatic sciences mainly examine nutrient enrichment and bloom dynamics. In contrast, tourism studies often treat blooms as episodic disturbances and rarely integrate exposure pathways, risk communication, or feedback to destination governance. This review synthesizes evidence across freshwater and marine systems to develop a coupled tourism–water ecosystem perspective. We link eutrophication drivers and bloom typologies to three dimensions. These are the degradation of tourism-supporting ecosystem services, compound health stressors, and communication filters. The first includes losses of water clarity and aesthetic value. The second involves multi-route exposure through contact, inhalation, and seafood ingestion. The third shapes perceived safety, trust, and behavioral adaptation. We further connect perceived health risks to observable tourist behaviors, including cancellation, destination substitution, and activity avoidance. These micro-level responses can aggregate into market-level demand contractions and consumption reallocation. They can also trigger regional economic cascades, including public management costs, employment impacts, and long-term reputational damage. Crucially, tourism is not merely a victim of blooms. It can also act as a reinforcing anthropogenic driver through wastewater burdens, infrastructure expansion, and pulse pressures. These pressures lower ecological resilience, especially under warming and hydrological stabilization. Finally, we identify governance leverage points. These include early-warning systems, threshold-based graded interventions, transparent risk communication, and integrated social–ecological modeling. These strategies can reduce uncertainty-driven losses and support adaptive destination management. Overall, this review reframes algal blooms as systemic social–ecological risks. It provides a structured basis for future empirical attribution and policy design in tourism-dependent waters under climate stress. Full article

Federated data spaces, established through initiatives such as IDSA and GAIA-X, enable organizations to share and monetize datasets under contractual terms. However, enforcing these contracts—particularly detecting unauthorized reuse or modification of datasets—remains an open challenge. We present the Off-Platform Contract Inspector, a component of the PISTIS framework, that implements a modular similarity-detection pipeline combining path-value Jaccard similarity, field-aware type-specific comparisons, and sentence-embedding-based semantic analysis across structured, semi-structured, and unstructured datasets. This contributes as follows: (i) an Inverse Document Frequency (IDF)-weighted structural similarity mechanism that discounts common domain vocabulary via Inverse Document Frequency weighting over the data space catalog, combined with a schema-evidence-gated fusion that reduces false positives from domain vocabulary overlap; (ii) an adaptive threshold optimization mechanism that learns modality-specific fusion weights and decision thresholds via cross-validated grid search; and (iii) a privacy-preserving similarity layer based on MinHash Locality-Sensitive Hashing signatures, Bloom filters with OR folding alignment, and Laplace noise for differential privacy, enabling cross-organizational dataset comparison without exposing raw data. Further, we contribute a threat taxonomy of seven dataset modification types ordered by detection difficulty, and evaluate the system on dataset pairs derived from real-world datasets across three smart-city application domains (Mobility, Energy, Automotive), with controlled augmentations applied to model adversarial behaviors. The IDF-weighted pipeline achieves high precision on intra-domain hard negatives—pairs of different tables from the same data space that share domain vocabulary—where text-similarity baselines produce false positives. The adaptive scheme learns per-modality fusion weights via cross-validated grid search. The privacy-preserving mode operates without accessing raw data and runs noticeably faster than the full pipeline, enabling screening while preserving data confidentiality. Full article

Due to the indispensable use of the internet, malicious actors have exploited URLs as a threat source of network information security and integrity. URL detection based on traditional methods has become inefficient against the uncontrolled increase of URLs, especially when facing dynamic and large-scale threats. To address the limitations of traditional methods and to provide intelligent and scalable detection of malicious URLs, this study proposes the hybrid framework (NetGuard) by integrating probabilistic data structures (PDSs) with machine learning (ML) capabilities. The proposed NetGuard utilizes PDSs to develop a Hybrid Scalable Detection Filter (HSDF), which combines the strengths of counting Bloom filters (CBFs) (deletion capability) and Scalable Bloom filters (SBFs). The proposed HSDF provides efficient membership queries under bounded false-positive rates (approximately 0.01) and ensures efficient data management and low-latency lookups on a scale of 10⁻⁵ s. On the other hand, NetGuard leverages the ML classifier capabilities to train and package a learned classifier for detecting malicious URLs. The proposed framework utilizes Decision Trees (DTs) and Random Forest (RF) classifiers. The proposed classifiers are trained by a novel SupURLsIdDs dataset which includes fifteen distinctive lexical and structural URL features extracted from four URL classes: benign, defacement, malware, and phishing URLs. The experimental results indicated the effectiveness of the HSDF in insertion and deletion operations, with minimal memory consumption (approximately 2.7 MB for 222,000 URLs) while maintaining a controlled false-positive rate (approximately 0.01 on Real-only subset up to 0.12 with synthetic data). The HSDF memory footprint represents a 99.88% enhancement compared to the RF model (which demands 2253.17 MB); thus, the HSDF complements RF as an ultra-lightweight first line of defense. The ML classifiers showed the superiority of RF, which achieved an overall classification accuracy of approximately 96% on large-scale URL data. These experiments are conducted using benchmark datasets constructed from aggregated real and synthetic data to demonstrate the scalability, adaptability, and resource efficiency of the first phase of NetGuard as a practical foundation for real-time web threat detection. The real-time integration and dynamic updates are presented as a deployment architecture and constitute future work. Full article

Adaptive recommendation mechanisms are widely used to personalise digital learning environments; however, many existing approaches prioritise algorithmic optimisation while providing limited insight into how recommendation behaviour aligns with pedagogically structured instructional artefacts, such as worksheets. To address this gap, this paper proposes a hybrid recommendation approach for adaptive worksheet generation that integrates content-based and collaborative filtering with explicit pedagogical constraints derived from Bloom’s Revised Taxonomy. The system ranks and selects learning and evaluation objects across cognitive levels by combining learner profiles, behavioural signals, and similarity-based information within a unified scoring framework. A simulation-based evaluation was conducted to examine the internal behaviour, stability, and instructional alignment of the recommendation engine under controlled conditions, using Bloom-aligned worksheets and synthetic learner profiles. The analysis focuses on expected–actual alignment and adaptive variation across cognitive levels rather than learning outcomes. Results indicate strong alignment with the intended instructional structure at lower cognitive levels, while bounded and interpretable adaptive variation emerges at higher levels. Evaluation object recommendations showed high agreement with the instructional design, exceeding 95% across simulated conditions. Overall, the study demonstrates how hybrid recommendation mechanisms can support adaptive content selection in pedagogically structured learning scenarios, offering a transparent and robust foundation for information-driven educational systems. Full article

Cyanobacterial blooms directly influence the structure and function of zooplankton communities; however, the trophic interactions between small tropical cladocerans and the cyanobacterium Dolichospermum are still poorly understood. We evaluated how two strains of Dolichospermum planctonicum (differing in filament length) affect the grazing rates of three tropical cladocerans with distinct size and prey spectra—Daphnia gessneri, Ceriodaphnia silvestrii, and Macrothrix paulensis—in single and mixed diets with the chlorophyte Monoraphidium capricornutum. Overall, grazing rates decreased as food concentration increased across all phytoplankton species. Daphnia was the most efficient filter-feeder in all diets, yet the responses to different-sized Dolichospermum strains varied between animals and diets. Shorter Dolichospermum was the least consumed food item in single diets, as opposed to what was observed in the mixed diets, where it was the most consumed. This reversal suggests that the mechanism limiting grazing on Dolichospermum might change drastically depending on the food context (availability of other food sources). Positive selectivity for both Dolichospermum and Monoraphidium was observed for all cladocerans. These findings highlight that the morphology of Dolichospermum planctonicum and the availability of alternative food sources during its blooms are critical regulators of grazing. The results also provide evidence of diverse feeding strategies of tropical cladocerans to prey on the filamentous cyanobacterium Dolichospermum planctonicum. Full article

Human land use in catchment areas has become a global concern due to its profound effects on water quality degradation. Associated eutrophication and algal bloom outbreaks in aquatic ecosystems pose an increasing threat to species that rely exclusively on water for foraging and breeding. In soda lakes, harmful algal blooms have caused fatal effects on lesser flamingos (Phoeniconaias minor), which are obligatory filter feeders and vital bio-indicators. However, little is known about how human land use affects nitrogen (N) and phosphorus (P) levels in soil and surface runoff at a watershed scale, particularly in human-dominated areas bordering the eastern African soda lakes. We aimed to understand how these levels differ between protected and unprotected land and how they might affect lesser flamingo foraging sources. We analyzed 72 surface soil and 13 surface runoff samples for N and P concentrations along valleys that potentially drain water into the Momella lakes, northern Tanzania. We found a higher soil P concentration in unprotected than in protected land, and at both sites, soil N and P concentrations were negatively related to slope. Water P concentrations in surface runoff from the unprotected land exceeded the United States Environmental Protection Agency recommended threshold (<0.1 mg/L), suggesting that human land use might negatively impact water quality and, thus, the foraging resources of flamingos in the Momella lakes. We recommend optimizing nutrient management strategies in the watershed to reduce nutrient enrichment from human-dominated areas in these unique soda lakes in Tanzania. Full article

Microbial communities are key regulators of ecological processes in aquatic ecosystems and serve as sensitive indicators of environmental change. Here, we investigated the diversity, assembly mechanisms, and spatial differentiation of bacterial and fungal communities across three representative regions of Dianchi Lake—a large, shallow, eutrophic plateau lake in Southwest China characterized by severe nutrient enrichment and organic pollution. The lake was divided into a submerged macrophyte remnant zone (SubmP), the heavily polluted Caohai area (hPollut), and a cyanobacterial bloom zone (HABs). Amplicon sequencing of the 16S rRNA and ITS genes revealed 7862 bacterial and 3141 fungal OTUs, spanning 69 bacterial phyla (1128 genera) and 9 fungal phyla (477 genera). Although 69 dominant bacterial genera (e.g., Flavobacterium) and 9 dominant fungal genera (e.g., Metschnikowia) were shared across regions, pronounced spatial heterogeneity was observed, primarily driven by total nitrogen and dissolved oxygen. Taxonomic richness and abundance were decoupled: rare (RT) and intermediate taxa (IT) accounted for the most richness, whereas abundant taxa (AT) dominated the total abundance but exhibited comparatively low diversity. IT and RT displayed significantly higher Shannon diversity and greater network robustness than AT; bacterial RT showed the highest robustness (0.35–0.45), while fungal IT demonstrated superior resilience. Community assembly was largely governed by stochastic processes (59–99% contribution), yet deterministic selection exerted stronger effects on IT and RT, particularly for bacteria in SubmP, where habitat heterogeneity enhanced environmental filtering. Functional prediction revealed distinct ecological strategies, with enhanced nitrogen cycling in hPollut, phototrophy in HABs, and pollutant degradation in SubmP. Collectively, these findings demonstrate that rare and intermediate taxa, rather than numerically dominant populations, underpin microbial stability and spatial differentiation in eutrophic lakes, highlighting the importance of nitrogen management and habitat heterogeneity in lake restoration. Full article

Pattern matching is a key technique in the physical verification of integrated circuit designs and is widely used in lithographic hotspot detection. Existing pattern-matching algorithms face challenges in effectiveness and robustness, especially when processing complex patterns. We propose a pattern-matching algorithm based on augmented vertex hashing that correctly handles polygons with holes and supports fuzzy matching. For exact matching, our method encodes Manhattan polygons into fixed-length hash values using the augmented vertex representation, enabling fast polygon comparisons. For fuzzy matching, we formulate the problem as a constellation problem over augmented vertices and solve it efficiently using a cache-friendly algorithm based on a Bloom filter. Experiments show that our approach is more than 5× faster than the current state of the art on average. Full article

Large language models (LLMs) with the Transformer architecture as the core have made significant progress in the field of natural language processing, and their application value in English teaching has also attracted much attention. In tasks such as text generation, question-answering systems, and translation, the processing capabilities of LLMs have significantly improved. However, existing LLMs have problems such as insufficient coverage of professional knowledge, rough semantic parsing, and weak personalized services. To address the aforementioned issues, this study proposes a dual-path retrieval-enhanced generation scheme that integrates vector databases and intelligent agents, aiming to improve the application of large models in English language teaching. Semantic retrieval of unstructured data in English teaching is realized through vector databases, knowledge is dynamically acquired by combining agents, and the accuracy is improved by using Bloom filters to fuse dual-path retrieval. At the same time, the retrieval efficiency is optimized by an importance-oriented algorithm, and user profiles are constructed based on multi-dimensional data to achieve personalized adaptation. Experiments show that the maximum optimization of the retrieval time of this scheme can reach 26.32%, and the highest retrieval accuracy can reach 86%. The key indicators and scores in tasks such as English knowledge retrieval and question-answering reasoning are better than those of the comparative schemes, providing an effective technical path for intelligent English teaching. Full article

While harmful cyanobacterial blooms (HCBs) are extensively characterized in eutrophic lakes, the ecological dynamics of connected river networks remain oversimplified, obscuring the mechanisms of community assembly and toxin distribution across the lake–river interface. This study investigated the spatial heterogeneity of HCBs and microcystins (MCs) in the Lake Taihu watershed, revealing a complex functional divergence between lotic and lentic ecosystems. The rivers functioned as primary nutrient sources, with Total Nitrogen (3.35 ± 1.52 mg·L⁻¹) and Total Phosphorus (0.21 ± 0.22 mg·L⁻¹) concentrations being 1.7-fold and 1.8-fold higher, respectively, than those in the lake during peak periods. Conversely, the lake acted as a biological sink, supporting a peak cyanobacterial density (3.32 × 10⁷ cells·L⁻¹) nearly 1.5 times that of the river network. Phytoplankton community analysis revealed distinct ecological niches: while the lentic lake environment was almost exclusively dominated by colonial Microcystis (>90% relative abundance), the lotic river networks harbored a diverse assemblage with significant contributions from filamentous Oscillatoria and Dolichospermum. Correspondingly, intracellular MC (IMC) in the lake (up to 14.5 μg·L⁻¹) significantly exceeded riverine levels (generally <1.0 μg·L⁻¹). Despite these compositional differences, toxin dynamics exhibited strong bidirectional coupling (r > 0.75, p < 0.01), suggesting a spillover effect where the lake determines the watershed’s toxin burden during rivers outflow period. Redundancy Analysis (RDA) further elucidated that limnetic blooms were primarily regulated by water temperature and pH, whereas riverine communities were strictly constrained by hydrodynamic flow. Consequently, the health risk assessment revealed a highly heterogeneous landscape where, beyond the northern lake bays, specific semi-lentic river segments emerged as cryptic hotspots. These findings demonstrate that while nutrients fuel the system, hydrodynamic conditions act as the ultimate ecological filter determining the spatiotemporal distribution of cyanobacterial risks, necessitating an integrated approach to monitoring the entire lake–river continuum. Full article

The progressive degradation of surface waters should become one of the most important problems requiring an urgent solution. One of the methods developed is filtering water through loose, degraded sediments, blooms of cyanobacteria or algae, or a bed of hemp (Cannabis sativa L.) waste or hemp fibers. The conducted tests on the percolation of water samples and/or water with sediment from surface waters at sites with different ecological statuses indicate the possibility of using hemp waste for the reclamation of water reservoirs and rivers. The effect of filtration is a rapid improvement in water quality and, consequently, an improvement in the ecological status. The best result was achieved for a small freshwater reservoir with a large number of algae and loose degraded sediment. The initial turbidity value was at the limit of the device’s measurement capability, reaching 9991 NTU. After filtration through the hemp waste bed, the turbidity dropped to 42.52 NTU, a 99.57% decrease. The remaining parameters, C, TDS, and pH, were not subject to significant variability as a result of filtering. Excessive amounts of organic matter, which create a problem for surface waters, are removed. Due to the carrier (hemp waste), which is organic waste, any possible release of small amounts into the aquatic environment will not pose a threat. After applying filtration, a decision can be made on further actions regarding the water reservoir or river: Self-renewal of the reservoir or further percolation using, for example, mill gauze or cleaning the reservoir with other, non-invasive methods. After the filtering procedure, the hemp waste, enriched with organic matter and water remaining in the waste, can be used for composting or directly for soil mulching (preliminary tests have yielded positive results). A hemp waste filter effectively removes Chronomus aprilinus larvae (Chrinomidae) from water. This result indicates the possibility of removing mosquito larvae in malaria-affected areas. The use of hemp filters would reduce the amount of toxic chemicals used to reduce mosquito larvae. Improving the ecological status of surface waters by filtering contaminants with hemp waste filters can reduce the need for chemical treatment. The use of natural, biological filters enables sustainable surface water management. This is crucial in today’s rapidly increasing chemical pollution of surface waters. Full article

Dynamic Searchable Encryption (DSE) is essential for enabling confidential search operations over encrypted data in cloud computing. However, all existing single-server DSE schemes are vulnerable to Keyword Pair Result Pattern (KPRP) leakage and fail to simultaneously achieve forward and backward security. To address these challenges, this paper proposes a conjunctive keyword DSE scheme based on a dual-server architecture (DS-CKDSE). By integrating a full binary tree with an Indistinguishable Bloom Filter (IBF), the proposed scheme adopts a secure index: The leaf nodes store the keywords and the associated file identifier, while the information of non-leaf nodes is encoded within the IBF. A random state update mechanism, a dual-state array for each keyword and the timestamp trapdoor designs jointly enable robust forward and backward security while supporting efficient conjunctive queries. The dual-server architecture mitigates KPRP leakage by separating secure index storage from trapdoor verification. The security analysis shows that the new scheme satisfies adaptive security under a defined leakage function. Finally, the performance of the proposed scheme is evaluated through experiments, and the results demonstrate that the new scheme enjoys high efficiency in both update and search operations. Full article

The Bloom filter remains one of the most influential constructs in probabilistic computation, a structure that achieves a mathematically elegant balance between accuracy, space efficiency, and computational speed. Since the original formulation of Dr. Burton H. Bloom in 1970, its design principles have expanded into a family of approximate membership query (AMQ) structures that now underpin a wide spectrum of modern computational systems. This review synthesizes the theoretical, algorithmic, and applied dimensions of Bloom filters, tracing their evolution from classical bit-vector models to contemporary learned and cryptographically reinforced variants. It further underscores their relevance in artificial intelligence and blockchain environments, where they act as relevance filters. Core developments, which include counting, scalable, stable, and spectral filters, are outlined alongside information-theoretic bounds that formalize their optimality. The analysis extends to adversarial environments, where cryptographic hashing and privacy-oriented adaptations enhance resilience under active attack, and to data-intensive domains such as network systems, databases, cybersecurity, and bioinformatics. Through the integration of historical insight and contemporary advances in learning, security, and system design, the Bloom filter emerges not merely as a data structure but as a unified paradigm for computation under uncertainty. The results presented in this review support practical advances in network traffic control, cybersecurity analysis, distributed storage systems, and large-scale data platforms that depend on compact and fast probabilistic structures. Full article

Environmental filtering studies have revealed immense oceanic microbial diversity, yet the Southeast Pacific remains comparatively undersampled. We characterize the molecular diversity of phytoplankton across two biogeographic domains with contrasting oceanography—fjords and channels (41–53° S) versus the open Pacific (36–42° S)—where the frequency and intensity of harmful algal blooms (HABs) have increased. Using SSU rRNA metabarcoding, we retrieved community composition and biogeographic patterns for micro-phytoplankton. Diversity signals indicated broadly overlapping communities between domains with subtle shifts along hydrographic and nutrient gradients rather than sharp breaks. Phylogenetic resolution within bloom-forming genera recovered well-supported clades, and multiple ASVs matched historically relevant HAB taxa, including representatives of the Alexandrium complex, Dinophysis, Pseudo-nitzschia, and Karenia. Together, these results suggest that regional environmental filtering acts modestly at the community level while preserving clear signals of taxa of management concern. By providing a regionally resolved, DNA-based baseline for southern Chile’s fjords and adjacent open coast, this study helps fill the molecular diversity gap for the Southeast Pacific and supports improved HAB surveillance and ecosystem forecasting in a climate-sensitive seascape. Full article

Satellite remote sensing is an important approach for monitoring lake water environments. However, in regions with frequent cloud and rainfall, optical remote sensing imagery often suffers from extensive data gaps caused by cloud cover, rainfall, and sun glint, which severely limit its continuity and reliability for long-term monitoring. To address this issue, this study uses Lake Taihu—a typical eutrophic lake located in a cloudy and rainy region—as a case study and systematically compares four representative gap-filling methods: Kriging Interpolation, Savitzky–Golay (SG) Filtering, Data Interpolating Empirical Orthogonal Functions (DINEOF), and the Data Interpolating Convolutional Auto Encoder (DINCAE). The results show that traditional methods retain some accuracy under low missing-data conditions (for Kriging: R = 0.84, RMSE = 7.85 μg/L; for SG Filtering: R = 0.88, RMSE = 6.67 μg/L), but tend to produce over-smoothing or distorted estimations in cases of extensive gaps or highly dynamic environments. In contrast, both DINEOF and DINCAE capture the spatiotemporal variability of chlorophyll-a more effectively, maintaining relatively high accuracy and robustness even when the missing rate exceeds 60% (for DINEOF: R = 0.84, RMSE = 6.91 μg/L; for DINCAE: R = 0.79, RMSE = 8 μg/L). Based on the optimal algorithm, a seamless long-term dataset of chlorophyll-a concentration covering Lake Taihu can be constructed, providing a solid data foundation for eutrophication trend analysis and algal bloom early warning. This study demonstrates the effectiveness of integrating statistical and deep learning approaches for lake water color remote sensing data reconstruction, offering important implications for enhancing continuous monitoring of lake water environments and supporting ecological management decisions. Full article