Search Results (707)

Submerged aquatic macrophytes play a key role in stream ecosystems, but their recovery in historically degraded Flemish streams is often limited. This study investigates whether sediment contamination constrains natural macrophyte germination and early seedling establishment. To address this knowledge gap, we combined a controlled mesocosm experiment with an analysis of long-term monitoring data from Flemish streams. The mesocosms showed that higher levels of sediment contamination reduced seedling emergence, indicating that sediment quality can directly inhibit germination and early establishment. In addition, historical monitoring data revealed only a weak association between sediment quality and macrophyte occurrence, pointing to the importance of interacting drivers such as hydrology, light availability, and habitat structure. Together, these findings highlight sediment contamination as a context-dependent but relevant barrier to macrophyte recruitment, underscoring the need to integrate sediment quality into broader restoration planning for streams in Flanders and abroad. Full article

Forecasting high-volume, univariate, and multivariate longitudinal data streams is a critical challenge in Big Data systems, especially with constrained computational resources and pronounced data variability. However, existing approaches often neglect multivariate statistical complexity (e.g., covariance, skewness, kurtosis) of multivariate time series or rely on recency-only windowing that discards informative historical fluctuation patterns, limiting robustness under strict resource budgets. This work makes two core contributions to big data forecasting. First, we establish a formal, multi-dimensional framework for quantifying “data bigness” across statistical, computational, and algorithmic complexities, providing a rigorous foundation for analyzing resource-constrained problems. Second, guided by this framework, we extend and validate the Adaptive High-Fluctuation Recursive Segmentation (AHFRS) algorithm for multivariate time series. By incorporating higher-order statistics such as covariance, skewness, and kurtosis, AHFRS improves predictive accuracy under strict computational budgets. We validate the approach in two stages. First, a real-world case study on a univariate Bitcoin time series provides a practical stress test using a Long Short-Term Memory (LSTM) network as a robust baseline. This validation reveals a significant increase in forecasting robustness, with our method reducing the Root Mean Squared Error (RMSE) by more than 76% in a challenging scenario. Second, its generalizability is established on synthetic multivariate data sets in Finance, Retail, and Healthcare using standard statistical models. Across domains, AHFRS consistently outperforms baselines; in our multivariate Finance simulation, RMSE decreases by up to 62.5% in Finance and Mean Absolute Percentage Error (MAPE) drops by more than 10 percentage points in Healthcare. These results demonstrate that the proposed framework and AHFRS advances the theoretical modeling of data complexity and the design of adaptive, resource-efficient forecasting pipelines for real-world, high-volume data ecosystems. Full article

The Diamond Open Access (OA) model—characterized by the absence of fees for both authors and readers—has gained increasing attention in recent years. A wide range of scholarly journals are using this model, as emerged while mapping the Diamond OA landscape worldwide; however, some still depend on hybrid revenue streams such as print sales, subscriptions, and marginal APCs. A number of recent initiatives underlined the need to increase quality assurance, sustainability, and cooperation within the Diamond OA ecosystem. Among them, the Diamond OA Standard (DOAS), a framework comprising detailed guidelines and a self-assessment tool to facilitate Diamond OA publishing practices, was created by the DIAMAS project, sponsored by the European Commission. Annali dell’Istituto Superiore di Sanità, the official journal of the Italian leading public health research institution, is a Diamond OA journal. To improve transparency and quality, the editorial team used the DOAS self-assessment tool to evaluate its compliance with the standards proposed by DIAMAS and to identify potential areas for improvement. This article presents the process and findings of the DOAS self-assessment tool conducted on Annali ISS, with the aim of sharing insights and support with other journals seeking to align with the DOAS framework. Full article

The rapid proliferation of the Internet of Things (IoT) has created an urgent need for on-device intelligence that balances high computational demands with stringent energy constraints. Existing edge inference frameworks struggle to deploy generative artificial intelligence (AI) models efficiently on low-power devices, often sacrificing fidelity for efficiency or lacking adaptability to dynamic conditions. To address this gap, we propose a generative AI edge inference framework integrating lightweight architecture compression, adaptive quantization, and energy-aware scheduling. Extensive experiments on CIFAR-10, Tiny-ImageNet, and IoT-SensorStream show that our method reduces energy consumption by up to 31% and inference latency by 27% compared with state-of-the-art baselines, while consistently improving generative quality. Robustness tests further confirm resilience under noise, cross-task, and cross-dataset conditions, and ablation studies validate the necessity of each module. Finally, deployment in a hospital IoT laboratory demonstrates real-world feasibility. These results highlight both the theoretical contribution of unifying compression, quantization, and scheduling, and the practical potential for sustainable, scalable, and reliable deployment of generative AI in diverse IoT ecosystems. Full article

Semiconductor wafer manufacturing is one of the most complex and data-intensive industrial processes, comprising 500–1000 tightly interdependent steps, each requiring nanometer-level precision. As device nodes approach 3 nm and beyond, even minor deviations in parameters such as oxide thickness or critical dimensions can lead to catastrophic yield loss, challenging traditional physics-based control methods. In response, the industry has increasingly adopted regression analysis and predictive modeling as essential analytical frameworks. Classical regression, long used to support design of experiments (DOE), process optimization, and yield analysis, has evolved to enable multivariate modeling, virtual metrology, and fault detection. Predictive modeling extends these capabilities through machine learning and AI, leveraging massive sensor and metrology data streams for real-time process monitoring, yield forecasting, and predictive maintenance. These data-driven tools are now tightly integrated into advanced process control (APC), digital twins, and automated decision-making systems, transforming fabs into agile, intelligent manufacturing environments. This review synthesizes foundational and emerging methods, industry applications, and case studies, emphasizing their role in advancing Industry 4.0 initiatives. Future directions include hybrid physics–ML models, explainable AI, and autonomous manufacturing. Together, regression and predictive modeling provide semiconductor fabs with a robust ecosystem for optimizing performance, minimizing costs, and accelerating innovation in an increasingly competitive, high-stakes industry. Full article

In today’s digital ecosystem, where millions of users interact with diverse online services and generate vast amounts of textual, transactional, and behavioral data, ensuring the trustworthiness of this information has become a critical challenge. Low-quality data—manifesting as incompleteness, inconsistency, duplication, or noise—not only undermines analytics and machine learning models but also exposes unsuspecting users to unreliable services, compromised authentication mechanisms, and biased decision-making processes. Traditional data quality assessment methods, largely based on manual inspection or rigid rule-based validation, cannot cope with the scale, heterogeneity, and velocity of modern data streams. To address this gap, we propose DQMAF (Data Quality Modeling and Assessment Framework), a generalized machine learning–driven approach that systematically profiles, evaluates, and classifies data quality to protect end-users and enhance the reliability of Internet services. DQMAF introduces an automated profiling mechanism that measures multiple dimensions of data quality—completeness, consistency, accuracy, and structural conformity—and aggregates them into interpretable quality scores. Records are then categorized into high, medium, and low quality, enabling downstream systems to filter or adapt their behavior accordingly. A distinctive strength of DQMAF lies in integrating profiling with supervised machine learning models, producing scalable and reusable quality assessments applicable across domains such as social media, healthcare, IoT, and e-commerce. The framework incorporates modular preprocessing, feature engineering, and classification components using Decision Trees, Random Forest, XGBoost, AdaBoost, and CatBoost to balance performance and interpretability. We validate DQMAF on a publicly available Airbnb dataset, showing its effectiveness in detecting and classifying data issues with high accuracy. The results highlight its scalability and adaptability for real-world big data pipelines, supporting user protection, document and text-based classification, and proactive data governance while improving trust in analytics and AI-driven applications. Full article

Phosphorus is a key macronutrient central to the processes of energy and information storage and exchange in the cell. Single-celled photosynthetic organisms, including microalgae, accumulate intracellular reserves of phosphorus (mostly in the form of polyphosphate) essential for the maintenance of cell homeostasis during fluctuations of external phosphorus availability. The polyphosphate reserves in microalgal cells are formed by polyphosphate polymerases—a ubiquitous enzyme family represented mainly by prokaryotic (PPK-type, typical of prokaryotes, e.g., cyanobacteria) and VTC-type polyphosphate polymerases harbored by eukaryotic microalgae, although certain species possess both PPK and VTC types of the enzyme. This enzyme is important for the environmental fitness of microalgae dwelling in diverse habitats, as well as for the efficiency of microalgae-based systems for the biocapture of phosphate from waste streams and for upcycling this valuable nutrient to agricultural ecosystems via biofertilizer from microalgal biomass. This review summarizes the recent progress in the field of structure, regulation, and functioning of VTC in microalgae. In conclusion, biotechnological implications and perspectives of VTC as a target of microalgal cell engineering and bioprocess design for improved phosphate bioremoval efficiency and culture robustness are considered. Full article

Acid mine drainage (AMD) is an environmental concern that needs to be addressed by some mining industries because of its high concentrations of metals and acidity that destroy affected ecosystems. Its formation typically persists beyond the operating life of a mine site. Its management is even more challenging for sites that are abandoned without rehabilitation. In this study, a legacy copper–gold mine located in Sto. Niño, Tublay, Benguet, Philippines, generating a copper- and manganese-rich AMD (Cu, maximum 17.2 mg/L; Mn, maximum 2.90 mg/L) at pH 4.59 (minimum) was investigated. With its remote location inhabited by the indigenous people local community (IPLC), a novel limestone-based hybrid passive treatment system that combines a limestone leach bed (LLB) and a controlled modular packed bed reactor (CMPB) has been developed from the laboratory and successfully deployed in the field while investigating the effective hydraulic retention time (HRT), particle size, and redox conditions (oxic and anoxic) in removing Cu and Mn and increasing pH. Laboratory-scale and pilot-scale systems using simulated and actual AMD, respectively, revealed that a 15 h HRT and both oxic and anoxic conditions were effective in treating the AMD. Considering these results and unsteady conditions of the stream in the legacy mine, a hybrid multi-stage limestone leach bed and packed bed were deployed having variable particle size (5 mm to 100 mm) and HRT. Regular monitoring of the system showed the effective removal of Cu (88.5%) and Mn (66.83%) as well as the increase of pH (6.26), addressing the threat of AMD in the area. Improvement of the lifespan of the system needs to be addressed, as issues of Cu-armoring were observed, resulting in reduced performance over time. Nonetheless, the study presents a novel technique in implementing passive treatment systems beyond the typical treatment trains reported in the literature. Full article

Stream bank erosion poses significant threats to societal well-being and ecosystem services. Despite its importance, studies in Greece have been limited. This study evaluated stream bank erosion categories using the geographic information system (GIS) and the Bank Erosion Hazard Index (BEHI). Five stream reaches with different characteristics were selected near Drama, Greece. The GIS was used to map the stream and riparian area characteristics and to locate the BEHI sampling plots. The BEHI was employed to classify bank erosion vulnerability. The Categorical Principal Components Analysis (CatPCA) analysis was used to determine the factors that influence erosion. The study reaches, except for one, had high, very high, and extreme stream bank erosion exceeding 28%. Two reaches had greater than 40% of the banks without erosion. Substantial differences in erosion categories (%) were detected due to different fluvio-geomorphologic and anthropogenic pressures. Based on the CatPCA, agricultural and urbanized riparian areas experienced high, very high, and extreme bank erosion. Reaches with perennial flow had limited erosion. In addition, straight reaches had many human interventions. Although mitigation measures had been taken, they have not been effective. Thus, the responsible authorities should consider adopting nature-based solutions to maintain and restore riverine and riparian areas. Full article

Fish secondary production integrates multiple demographic parameters, including population density, growth, mortality, and recruitment, and thereby provides a comprehensive measure of ecological performance. It is also a valuable tool for assessing the ecological integrity of stream ecosystems and the responses of fish populations to habitat alteration, climatic variability, and anthropogenic pressures. Despite its relevance, empirical estimates of fish production remain limited due to methodological constraints. In this study, we quantified secondary production and production-to-biomass (P/B) ratios for Mediterranean brown trout (Salmo trutta complex) across six headwater stream reaches in the northeastern Iberian Peninsula, characterized by contrasting hydrological regimes, channel morphology, and water chemistry. Field sampling was conducted over two consecutive annual cycles (2008/2009 and 2009/2010) at all sites, with extended monitoring at two reaches until 2017 to assess long-term variability. Annual trout production, over the two consecutive annual cycles, ranged from 30.9 to 167.8 kg ha⁻¹ year⁻¹ (mean = 82.2 kg ha⁻¹ year⁻¹), and mean P/B ratios ranged from 0.61 to 1.13 (mean = 0.80). These values fall within the intermediate range reported for brown trout globally and reflect the constrained energy dynamics of Mediterranean streams. Higher production was generally associated with strong age-1 recruitment, elevated standing biomass, and greater water alkalinity. Long-term analyses revealed that interannual variation in trout production was significantly correlated with discharge variability, with higher production occurring under more stable flow conditions. However, in addition to flow variability other factors, such as habitat complexity, may modulate local productivity. Consequently, interannual fluctuations at the long-term sites revealed substantial demographic variability influenced by site-specific environmental conditions. These findings offer reference baselines for Mediterranean trout populations and contribute to the ecological basis for their conservation and management. Full article

Bare sand patches are extensively distributed in dryland ecosystems, and their spatiotemporal evolution provides critical insights into regional eco-environmental changes. The Mu Us Sandy Land, a typical dryland region, exemplifies a distinctive mosaic distribution of bare sand and vegetation patches. Based on the Google Earth Engine (GEE) platform and Landsat time-series imagery (1986–2023), this study extracted multi-temporal bare sand patches using the random forest algorithm. We quantified their spatiotemporal dynamics and identified driving mechanisms through integration with natural/socioeconomic datasets. Key findings include the following: (1) The total area of bare sand patches decreased significantly after 2000, with an average annual reduction of 530.08 km² (p < 0.01), a rate markedly exceeding pre-2000 rates. (2) Before 2000, bare sand patches were widespread across the entire region; however, by 2023, only residual patches persisted in the northwestern regions. (3) The most significant reduction in bare sand patch area is attributable to the shrinkage of giant patches (>10 km²). (4) The spatial distribution of bare sand patches is primarily controlled by a combination of natural factors, including stream, precipitation, topography, and wind regime. (5) The principal drivers of the reduction in bare sand patch area are anthropogenic activities, such as the implementation of ecological restoration projects, advancements in agricultural technology, and transformations in breeding patterns. These findings provide a scientific foundation for desertification control and ecosystem management strategies in drylands. Full article

Different sampling techniques were evaluated to assess potential differences in species richness and the abundances of phytoplankton across several lowland aquatic environments. Five sampling methods were used, including a bucket, narrow- and wide-mouth bottles, a 10 µm plankton net, and a vertical Van Dorn bottle. These sampling methods were applied in subtropical streams, shallow lakes, and rivers. The results were compared using a two-way ANOVA to evaluate differences in total density by considering the morphological group and major phytoplankton phyla. Similarity analyses (SIMPER) and a permutational multivariate analysis of variance (PERMANOVA) were performed to compare the relative abundances of the species. The results showed, in general (except with Cyanophyta, Chrysophyta, and colonies—coenobia), significant differences in the effect of the sampling method but without interaction with the kind of environment. Particularly, the plankton net always reported lower density estimations, with the bucket having the highest values and the wide–narrow bottle methods having similar values. SIMPER and PERMANOVA indicated differences, especially with the plankton net and the other methods, particularly the bucket. These findings suggest that the sampling method can influence species counts and registration in subtropical water ecosystems, highlighting the need for standardized procedures across countries to obtain comparable and reliable results. Full article

Microplastics (MPs) have emerged as persistent pollutants in urban freshwater ecosystems, yet their vertical distribution in stream sediments remains underexplored. This study investigated MPs at 5 cm and 10 cm depths across 17 sites in Branch Brook Park, Newark, NJ, during three sampling periods in 2022 and 2023. MPs were extracted through density separation and quantified using FTIR and Raman spectroscopy. The MP concentrations in stream sediments ranged from 560 to 3930 p/kg of dry sediment, with significantly higher abundances observed at 5 cm depth. The surface sediments consistently accumulated more MPs, especially during dry seasons, highlighting limited vertical infiltration under low-saturation conditions. The longitudinal spatial distribution did not show a notable trend along the urban stream course. Furthermore, there was a significant difference in MP accumulation between the three sampling periods, indicating a seasonal and temporal variation. The regression analyses showed weak correlations between MP concentrations and environmental parameters such as pH (R² = 0.02) and temperature (R² = 0.05), suggesting that physicochemical conditions alone exert limited control on MP accumulation compared to localized hydrological and land-use factors. These findings provide new insights and highlight the need for depth-integrated monitoring strategies and targeted pollution mitigation at stormwater entry points. Full article

The rapid rise of live streaming e-commerce has transformed retail dynamics; however, the allocation of after-sales service costs between live streaming salespeople and manufacturers remains a critical, unresolved issue, exacerbated by fairness concerns among stakeholders. Utilizing a Stackelberg game model where manufacturers act as leaders and live streaming salespeople as followers, this study examines the impact of cost allocation on profit distribution and supply chain efficiency. The framework incorporates a coefficient for fairness concerns and an after-sales effort to develop nine decision-making scenarios. Analysis demonstrates that perceptions of fairness significantly reshape cost-sharing strategies: when manufacturers assume after-sales responsibilities, their scale effects reduce marginal costs, maximizing overall supply chain profit. Conversely, when a live streaming salesperson bears costs, excessive focus on fairness reduces total supply chain efficiency, even if short-term profits are gained through premium pricing. These results validate that the Stackelberg game model combined with fairness concerns and after-sales efforts balances efficiency–profit dual objectives, providing a sustainable governance framework for live streaming e-commerce ecosystems. Full article

Our current knowledge of the cyanobacterial diversity in Egypt is still underestimated During our routine study on Egyptian cyanobacteria, two interesting and morphologically cryptic strains were isolated from streams of Bahr Yussef and Qarun Lake, one of the oldest lakes in the world, located at the Faiyum depression, Egypt. We applied the polyphasic approaches, combining the state-of-the-art morphotaxonomy, 16S rRNA gene phylogenies, and ecological preferences to precisely unravel the taxonomic positions of these two cyanobacterial strains. Based on a combination of their morphotaxonomic traits and 16S rRNA phylogenetic assessment, we identified them as Alkalinema pantanalense (Leptolyngbyaceae, Leptolyngbyales) and Roholtiella edaphica (Nostocaceae, Nostocales). Both species are considered new cyanobacterial records for Egypt and the African continent based on the available literature. From an ecological standpoint, both species are eutraphentic, where they could tolerate relatively elevated concentrations of NO₃⁻, NH₄⁺ (in particular for R. edaphica), and silicates, reflecting eutrophication signs in the ecosystems they colonize. This study adds to the limited molecular information available on the Egyptian cyanobacteria, and also highlights the need for re-investigation of Egyptian cyanobacteria, using polyphasic approaches, to better understand their taxonomy and ecology. Full article