Search Results (2,030)

AI technology is reshaping the knowledge ecosystem, bringing both challenges and opportunities to libraries. This article examines the transformation of librarians from “knowledge guardians” to “intelligent collaborators.” It discusses the professional challenges and practical dilemmas introduced by AI through the lenses of value reorientation and paradigm shift. The paper argues that librarians should actively adopt new technologies, engage in ongoing learning, and develop more resilient knowledge service systems, while also identifying their key roles and potential pathways for transformation within smart library frameworks. Full article

Bats are ecologically vital mammals, serving as pollinators, seed dispersers, and bioindicators of ecosystem health. Many species inhabit natural caves, which offer optimal conditions for survival but present challenges for direct ecological monitoring due to their dark, complex, and inaccessible environments. Traditional monitoring methods, such as mist-netting, are invasive and limited in scope, highlighting the need for non-intrusive alternatives. In this work, we present a portable multisensor platform designed to operate in underground habitats. The system captures multimodal data, including near-infrared (NIR) imagery, ultrasonic audio, 3D structural data, and RGB video. Focusing on NIR imagery, we evaluate the effectiveness of the YOLO object detection framework for automated bat detection and counting. Experiments were conducted using a dataset of NIR images collected in natural shelters. Three YOLO variants (v10, v11, and v12) were trained and tested on this dataset. The models achieved high detection accuracy, with YOLO v12m reaching a mean average precision (mAP) of 0.981. These results demonstrate that combining NIR imaging with deep learning enables accurate and non-invasive monitoring of bats in challenging environments. The proposed approach offers a scalable tool for ecological research and conservation, supporting population assessment and behavioral studies without disturbing bat colonies. Full article

As a perceptual representation of ecosystem structure and function, the soundscape has become an important indicator for evaluating ecological health and assessing the impacts of human disturbances. Understanding the spatiotemporal heterogeneity of soundscapes is essential for revealing ecological processes and human impacts in protected areas. This study investigates such heterogeneity in Zhangjiajie National Forest Park using deep learning approaches. To this end, we constructed a dataset comprising eight representative sound source categories by integrating field recordings with online audio (BBC Sound Effects Archive and Freesound), and trained a classification model to accurately identify biophony, geophony, and anthrophony, which enabled the subsequent analysis of spatiotemporal distribution patterns. Our results indicate that temporal variations in the soundscape are closely associated with circadian rhythms and tourist activities, while spatial patterns are strongly shaped by topography, vegetation, and human interference. Biophony is primarily concentrated in areas with minimal ecological disturbance, geophony is regulated by landforms and microclimatic conditions, and anthrophony tends to mask natural sound sources. Overall, the study highlights how deep learning-based soundscape classification can reveal the mechanisms by which natural and anthropogenic factors structure acoustic environments, offering methodological references and practical insights for ecological management and soundscape conservation. Full article

Wetlands are natural ecosystems of great ecological and economic value. They provide undeniable ecosystem services that contribute to promoting sustainable development. Exposed to different pressures, these limnic ecosystems are particularly vulnerable to climate change. Thus, assessing wetland vulnerability is of utmost importance. Based on a systematic selection of relevant peer-reviewed studies, this paper helps to develop a general vision of the methods used to assess wetland vulnerability in different contexts, emphasizing the use of advanced computational approaches. Hence, an overview of different cases of wetlands all across the five continents and of different types of habitats is presented. Whether the wetland is permanently or seasonally flooded, coastal, or tropical, this study enables the analysis of diverse, already established vulnerability evaluation index systems. Some of these indices were computed using geographic information systems (GISs), artificial intelligence (AI), machine learning (ML), spatial principal component analysis (SPCA) and driver–pressure–state–impact–response (DPSIR) as evaluation models. Indeed, given the adoption of different methods, diverse models, and analytical approaches under different scenarios, the vulnerability assessment process should be seen as an iterative rather than a definitive process. An accurate wetland vulnerability assessment is essential for ensuring the sustainability of wetland ecosystems and for informing effective conservation and management strategies. Full article

The convergence of decentralized artificial intelligence (DAI), blockchain technology, and smart contracts is reshaping the design and governance of intelligent systems. As these technologies rapidly evolve, addressing privacy within their architecture, usage models, and associated risks has become increasingly critical. This systematic literature review examines architectural patterns, governance frameworks, real-world applications, and persistent challenges in DAI systems. It identifies prevailing designs such as federated learning integrated with consensus protocols, smart contract-based incentive mechanisms, and decentralized verification methods. Drawing from a diverse body of recent literature, the review highlights implementations across sectors, including healthcare, finance, IoT, autonomous systems, and intelligent infrastructure, each demonstrating significant contributions to privacy, security, and collaborative innovation. Despite these advancements, DAI systems face ongoing obstacles such as scalability limitations, privacy trade-offs, and difficulties with regulatory compliance. The review emphasizes the need for integrative governance approaches that balance transparency, accountability, incentive alignment, and ethical oversight. These elements are proposed as co-evolving pillars essential to establishing trustworthiness in decentralized AI ecosystems. This work offers a comprehensive review for understanding the current landscape and guiding the development of responsible and effective DAI systems in the Web3 era. Full article

Accurate extraction of water bodies in remote sensing images is crucial for natural disaster prediction, aquatic ecosystem monitoring, and resource management. However, most existing deep-learning-based methods primarily operate in the raw pixel space of images and fail to leverage the frequency characteristics of remote sensing images, resulting in an inability to fully exploit the representational power of deep models when predicting mask images. This paper proposes a Frequency-Band Decoupling Detail Enhancement Network (FDEN) to achieve high-precision water body extraction. The FDEN begins with an initial decoupling and enhancement stage for frequency information. Based on this multi-frequency representation, we further propose a Multi-Band Detail-Aware Module (MDAM), designed to adaptively enhance salient structural cues for water bodies across frequency bands while effectively suppressing irrelevant or noisy components. Extensive experiments demonstrate that the FDEN model outperforms state-of-the-art methods in terms of its segmentation accuracy and robustness. Full article

The estimation of accurate and precise Satellite-Derived Bathymetries (SDBs) is important in marine and coastal applications for a better understanding of the ecosystems and science-based decision-making. Despite the advancements in related Machine Learning (ML) studies, quantifying the anticipated bias per pixel in the SDBs remains a significant challenge. This study aims to address this knowledge gap by developing a spatially explicit uncertainty index of a ML-derived SDB, capable of providing a quantifiable anticipation for biases of 0.5, 1, and 2 m. In addition, we explore the usage of this index for model optimization via the exclusion of training points of high or moderate uncertainty via a six-fold iteration loop. The developed methodology is applied across the national coastal extent of Belize in Central America (~7017 km²) and utilizes remote sensing data from the European Space Agency’s twin satellite system Sentinel-2 and Planet’s NICFI PlanetScope. In total, 876 Sentinel-2 images, nine NICFI six-month basemaps and 28 monthly PlanetScope mosaics are processed in this study. The training dataset is based on NASA’s system Ice, Cloud and Elevation Satellite (ICESat-2), while the validation data are in situ measurements collected with scientific equipment (e.g., multibeam sonar) and were provided by the National Oceanography Centre, UK. According to our results, the presented approach is able to provide a pixel-based (i.e., spatially explicit) uncertainty index for a specific prediction bias and integrate it to refine the SDB. It should be noted that the efficiency of the optimization of the SDBs as well as the correlations of the proposed uncertainty index with the absolute prediction error and the true depth are low. Nevertheless, spatially explicit uncertainty information produced by a ML-related SDB provides substantial insight to advance coastal ecosystem monitoring thanks to its capability to showcase the difficulty of the model to provide a prediction. Such spatially explicit uncertainty products can also aid the communication of coastal aquatic products with decision makers and provide potential improvements in SDB modeling. Full article

Ecosystem services are crucial for animals, plants, the planet, and human well-being. Decreasing biodiversity and environmental destruction of ecosystems will have severe consequences. Designing technologies that could support, enhance, or even replace ecosystem services is a complex task that the Manufactured Ecosystems Project team considers to be only achievable with transdisciplinarity, as it unlocks new directions for designing research and development systems. One of these directions in the project is bio-inspiration, learning from natural systems as the foundation for manufacturing ecosystem services. Using soil formation as a case study, text-mining of existing scientific literature reveals a critical gap: fewer than 1% of studies in biomimetics address soil formation technological replacement, despite the rapid global decline in natural soil formation processes. The team sketches scenarios of ecosystem collapse, identifying how bio-inspired solutions for equitable and sustainable innovation can contribute to climate adaptation. The short communication opens the discussion for collaboration and aims to initiate future research. Full article

Grassland aboveground biomass (AGB) serves as a critical indicator of ecosystem productivity and carbon cycling, playing a pivotal role in ecosystem functioning. The advances in hyperspectral and terrestrial Light Detection and Ranging (LiDAR) data have provided new opportunities for grassland AGB monitoring, but current research remains predominantly focused on data-driven machine learning models. The black-box nature of such approaches resulted in a lack of clear interpretation regarding the coupling relationships between these two data types in grassland AGB estimation. For grassland aboveground fresh biomass, the theoretical estimation can be decomposed into either the product of planar density (PD) and plot area or the product of stereoscopic density (SD) and grassland community volume. Based on this theory, our study developed a semi-mechanistic remote sensing model for grassland AGB estimation by integrating hyperspectral-derived biomass density with extracted structural parameters from terrestrial LiDAR. Initially, we built hyperspectral estimation models for both PD and SD of grassland fresh AGB using PLSR. Subsequently, by integrating the inversion results with grassland quadrat area and community volume measurements, respectively, we achieved quadrat-scale remote sensing estimation of grassland AGB. Finally, we conducted comparative accuracy assessments of both methods across different phenological stages to evaluate their performance differences. Our results demonstrated that SD, which incorporated structural features, could be more precisely estimated (R² = 0.90, nRMSE = 7.92%, Bias% = 0.01%) based on hyperspectral data compared to PD (R² = 0.79, nRMSE = 10.19%, Bias% = −7.25%), with significant differences observed in their respective responsive spectral bands. PD showed greater sensitivity to shortwave infrared regions, while SD exhibited stronger associations with visible, red-edge, and near-infrared bands. Although both methods achieved comparable overall AGB estimation accuracy (PD-based: R² = 0.79, nRMSE = 10.19%, Bias% = −7.25%; SD-based: R² = 0.82, nRMSE = 10.58%, Bias% = 1.86%), the SD-based approach effectively mitigated the underestimation of high biomass values caused by spectral saturation effects and also demonstrated superior and more stable performance across different growth periods (R² > 0.6). This work provided concrete physical meaning to the integration of hyperspectral and LiDAR data for grassland AGB monitoring and further suggested the potential of multi-source remote sensing data fusion in estimating grassland AGB. The findings offered theoretical foundations for developing large-scale grassland AGB monitoring models using airborne and spaceborne remote sensing platforms. Full article

Phytoplankton plays a pivotal role in marine ecosystems and global biogeochemical cycles. Accurate identification and monitoring of phytoplankton are essential for understanding environmental dynamics and climate variations. Despite the significant progress made in automatic phytoplankton identification, current datasets predominantly consist of idealized laboratory images, leading to models that demonstrate persistent limitations in the fine-grained differentiation of phytoplankton species. To achieve high accuracy and transferability for morphologically similar species and diverse ecosystems, we introduce a hybrid dataset by integrating laboratory-based observations with in situ marine environmental data. We evaluate the performance of our dataset on contemporary deep learning models, revealing that CNN-based architectures offer superior stability (85.27% mAcc., 93.76% oAcc.). Multimodal learning facilitates refined phytoplankton recognition through the integration of visual and textual representations, thereby enhancing the model’s semantic comprehension capabilities. We present a fine-tuned visual language model leveraging enhanced textual prompts augmented with expert-annotated morphological descriptions, significantly enhancing visual-semantic alignment and allowing for more accurate and interpretable recognition of closely related species (84.11% mAcc., 94.48% oAcc.). Our research establishes a benchmark dataset that facilitates real-time ecological monitoring and aquatic biodiversity research. Furthermore, it also contributes to the field by enhancing model robustness and transferability to diverse environmental contexts and taxonomically similar species. Full article

Water hyacinth (Eichhornia crassipes), native to South America, is a highly invasive aquatic plant threatening freshwater ecosystems worldwide. Its rapid proliferation negatively impacts water quality, biodiversity, and navigation. Remote sensing offers an effective means to monitor such aquatic environments by providing extensive spatial and temporal coverage with improved resolution. This systematic review examines remote sensing applications for monitoring water hyacinth and water quality in studies published from 2014 to 2024. Seventy-eight peer-reviewed articles were selected from the Web of Science, Scopus, and Google Scholar following strict criteria. The research spans 25 countries across five continents, focusing mainly on lakes (61.5%), rivers (21%), and wetlands (10.3%). Approximately 49% of studies addressed water quality, 42% focused on water hyacinth, and 9% covered both. The Sentinel-2 Multispectral Instrument (MSI) was the most used sensor (35%), followed by the Landsat 8 Operational Land Imager (OLI) (26%). Multi-sensor fusion, especially Sentinel-2 MSI with Unmanned Aerial Vehicles (UAVs), was frequently applied to enhance monitoring capabilities. Detection accuracies ranged from 74% to 98% using statistical, machine learning, and deep learning techniques. Key challenges include limited ground-truth data and inadequate atmospheric correction. The integration of high-resolution sensors with advanced analytics shows strong promise for effective inland water monitoring. Full article

Background: Recent studies have shown that gut microbiota have important roles in different human diseases. There has been an ever-increasing application of high-throughput technologies for the characterization of microbial ecosystems. This led to an explosion of various molecular profiling data, and the analysis of such data has shown that machine-learning algorithms have been useful in identifying key molecular signatures. Results: In this review, we first analyze how dysbiosis of the intestinal microbiota relates to human disease and how possible modulation of the gut microbial ecosystem may be used for disease intervention. Further, we introduce categories and the workflows of different machine-learning approaches and how they perform integrative analysis of multi-omics data. Last, we review advances of machine learning in gut microbiome applications and discuss challenges it faces. Conclusions: We conclude that machine learning is indeed well suited for analyzing gut microbiome and that these approaches are beneficial for developing gut microbe-targeted therapies, helping in achieving personalized and precision medicine. Full article

This perspective article presents an innovative concept for a biomanufacturing Knowledge Hub (KH), designed as a data-driven learning platform supporting the entire lifecycle of biomedical products. By integrating advanced data sharing and processing technologies, the KH aspires to connect patients, bioengineers, clinicians, regulators, companies, and investors to accelerate product development, reduce redundancies, and ultimately fast-track the delivery of biomedical innovations to patients. We discuss current challenges in accessing and sharing data within biomanufacturing and outline novel approaches for building an ecosystem that links data stores, integrates digital twins, and leverages advanced analytics. The KH offers transformative capabilities, enabling the development of new products at a substantial increased speed. It is built as a secure, quantum-resistant platform that encrypts data and allows access through advanced algorithms, creating an intelligent, collaborative environment. Users can harness collective knowledge to enhance products, launch innovations, integrate technologies, and unlock revenue opportunities based on data quality and usage. This KH aims to revolutionize biomanufacturing, offering unprecedented opportunities for innovation, better patient outcomes, and commercialization with far reaching applications beyond biomanufacturing in the future. Full article

The study undertook a comprehensive review and comparative analysis of natural language processing techniques for news article classification, with a particular focus on Java language libraries. The dataset comprised an excess of 200,000 items of news metadata sourced from The Huffington Post. The traditional algorithms based on mathematical statistics and deep machine learning were evaluated. The libraries chosen for tests were Apache OpenNLP, Stanford CoreNLP, Waikato Weka, and the Huggingface ecosystem with the Pytorch backend. The efficacy of the trained models in forecasting specific topics was evaluated, and diverse methodologies for the feature extraction and analysis of word-vector representations were explored. The study considered aspects such as hardware resource management, implementation simplicity, learning time, and the quality of the resulting model in terms of detection, and it examined a range of techniques for attribute selection, feature filtering, vector representation, and the handling of imbalanced datasets. Advanced techniques for word selection and named entity recognition were employed. The study compared different models and configurations in terms of their performance and the resources they consumed. Furthermore, it addressed the difficulties encountered when processing lengthy texts with transformer neural networks, and it presented potential solutions such as sequence truncation and segment analysis. The elevated computational cost inherent to Java-based languages may present challenges in machine learning tasks. OpenNLP model achieved 84% accuracy, Weka and CoreNLP attained 86% and 88%, respectively, and DistilBERT emerged as the top performer, with an accuracy rate of 92%. Deep learning models demonstrated superior performance, training time, and ease of implementation compared to conventional statistical algorithms. Full article

Climate change significantly affects hydrological processes in forest ecosystems, particularly in sensitive coastal areas such as the Croatan National Forest (CNF) in North Carolina. Accurate projections of future water yield are essential for managing agriculture, forestry, and natural ecosystems. This study investigates the potential impacts of climate change on water yield using a combination of statistical downscaling and machine learning. Two downscaling methods, a Statistical DownScaling Model (SDSM) and Multivariate Adaptive Constructed Analogs (MACA), were evaluated, with the SDSM providing superior performance for local climate conditions. To improve precipitation input accuracy, twenty ensemble scenarios were generated using the SDSM, and various machine learning algorithms were applied to identify the optimal ensemble. Among these, the Extreme Gradient Boosting (XGBoost) algorithm exhibited the lowest error and was selected for producing high-quality precipitation time series. This methodology is integrated into the MIDAS (Machine Learning-Based Integration of Downscaled Projections for Accurate Simulation) approach, which leverages machine learning to enhance climate input precision and reduce uncertainty in hydrological modeling. Water yield was simulated over the period 1961–2060, combining observed and projected climate data to capture both historical trends and future changes. The results show that combining statistical downscaling with machine learning algorithms can help improve the accuracy of water yield projections under climate change and be useful for water resource planning, forest management, and climate adaptation. Full article