Search Results (1,036)

This study conducts a multi-objective optimization of an impact pruner for coniferous plantation trees, using Prince Rupprecht’s larch (Larix principis-rupprechtii Mayr) in North China as a case study. The objective is to establish an impact cutting mechanics model and to construct an impact cutting platform. This study utilizes the Box–Behnken principle, with the cutting speed (v), cutter wedge angle (β), and cutting clearance (L) as influencing factors and the cutting energy consumption (Y₁), total equipment energy consumption (Y₂), and specific cutting area (S) as evaluation indexes. The cutting parameters were optimized using a mathematical model for multi-objective optimization. The experimental results indicate that the factors influencing target Y₁ were ranked as β, L, and v, while the factors influencing target Y₂ were ranked as β, v, and L, and the factors influencing target S were ranked as L, β, and v. Field tests demonstrated that the optimization reduced the cutting energy consumption by up to 16.90% and improved the cutting quality by up to 19.28%. These gains directly translate to improved operational efficiency and economic value in forestry management. The optimal parameters corresponding to these improvements are v = 2.15 m·s⁻¹, β = 20°, and L = 5 mm, resulting in Y₁ = 36.10 J, Y₂ = 3351.01 J, and S = 3.45. These results demonstrate the feasibility and efficiency of the impact pruning method for Larix principis-rupprechtii in coniferous plantation forests. By combing mechanism analysis with multi-objective optimization, this study proposes a solution that can improve the pruning quality of coniferous plantation trees, reduce the energy consumption of impact pruning machines, enhance tree health, and serve as a measure to prevent pests and diseases, contributing to the advancement of artificial forest plant protection technology. Full article

PM_2.5 is a major air pollutant that threatens urban air quality and public health. Its concentration is influenced by both meteorological conditions and air pollutants, exhibiting complex nonlinear and temporal characteristics. Traditional statistical methods are limited in their ability to model complex relationships among environmental variables, while machine learning models still require improvements in hyperparameter optimization and interpretability. Therefore, developing an accurate and interpretable PM_2.5 estimation model remains an important research objective. This study used daily air-quality and meteorological data collected in Wuhan from 2016 to 2025 to develop six machine learning models: Decision Tree (DT), Random Forest (RF), XGBoost, LightGBM, Support Vector Machine (SVM), and Multilayer Perceptron (MLP). The Particle Swarm Optimization (PSO) algorithm was employed to optimize the hyperparameters of these models. By comparing the root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE) of each model on both the training and test sets, the PSO-MLP model was identified as the best-performing model. Furthermore, the Shapley Additive Explanations (SHAP) method was applied to perform both global and local interpretation analyses of the best-performing model. The results indicate that the PSO-MLP model achieved the highest estimation performance among all evaluated models, with an R² value of 0.746 on the test set. SHAP analysis revealed that CO, Temperature (Temp), and NO₂ were the most influential predictors, while all variables exhibited distinct nonlinear relationships with PM_2.5 concentration. These findings may contribute to PM_2.5 concentration estimation, air-quality management, and environmental decision-making. Full article

African soils face increasing levels of metal pollution due to industrialization, artisanal mining activities, improper waste management, and enhanced agricultural productivity. However, unlike many organic pollutants, heavy metals do not degrade naturally and therefore persist in environmental systems for prolonged periods. Heavy metals accumulate over many decades in the soil and bioaccumulate through the food chain causing severe health complications such as cancer, kidney problems, and neurological impairment. This paper reviews the current literature on the origin, prevalence, and behavior of the main pollutants Pb, Cd, Cr, As, Hg, and Cu. The major phytoremediation methods including phytoextraction, rhizofiltration, phytostabilization, and phytovolatilization are highlighted alongside in planta screening methods for hyperaccumulating plants including Berkheya coddii (Ni) and Haumaniastrum robertii (Co). The paper evaluates various enhancement techniques such as the use of chelators, Rhizobium inoculations, and genetic modifications. The significance of these approaches in tropical and subtropical climates is discussed. The paper suggests a holistic framework involving empirical kinetic modeling, geospatial machine learning (random forest, kriging), and molecular omics in prediction modeling. Major hurdles in such predictions include lack of field-based verification of the models, biotechnology safety of genetically modified (GM) organisms, and inadequate regulations. Future perspectives emphasize community-driven phytomining, biomass recycling, and resilient phytoremediation solutions. Full article

In artificial breeding of forest musk deer (Moschus berezovskii), common diseases such as abscess, enteritis, pneumonia, and parasitic infections exhibit persistently high morbidity rates. The early symptoms of certain diseases are often insidious and difficult to discern. Conventional manual inspection routines not only fail to achieve accurate diagnosis but also frequently disturb the animals, induce stress responses, and consequently delay optimal treatment windows. To address this practical challenge, this study employs an improved BRW-GPLinker joint entity-relationship extraction approach to perform integrated extraction and structural organization of disease entities, symptom manifestations, etiological associations, and preventive and therapeutic measures from farming literature and clinical records, thereby constructing a disease knowledge graph for forest musk deer. Through the introduction of a Boundary-Aware Module for refined entity boundary detection, a Relative Distance Bias Module to mitigate pairing errors in dense contexts, and a Weighted Sparse Multi-label Cross-Entropy loss function to enhance recall for infrequent relations, the proposed model achieves an F1 score of 0.887 on a self-constructed dataset and demonstrates favorable generalization capability on medical-domain datasets. By transforming fragmented clinical logs and manuals into structured medical associations, this knowledge graph facilitates rapid retrieval of forest musk deer disease information, thereby enhancing veterinary decision-making efficiency and assisting forest musk deer health management. Full article

Water quality monitoring is a fundamental element of sustainable aquaculture management, as changes in parameters of physicochemical and biological properties directly affect the health, growth performance, and productivity of the aquaculture systems. Although traditional machine learning (ML) methods have demonstrated effectiveness in water quality classification, their performance often depends on large amounts of labeled data, which can be challenging and expensive to collect in real-world aquaculture environments. This study explores a few-shot learning (FSL) framework for data-efficient water quality classification under limited supervision to address this limitation. Several FSL models, including prototypical networks (ProtoNet), Siamese Networks, and Matching Networks were developed and evaluated in a comparative experimental framework against the traditional machine learning classifiers logistic regression, random forest, support vector machine and extreme gradient boosting. Low-data learning scenarios were simulated using a structured episodic evaluation approach. Experimental results demonstrate FSL techniques outperform traditional machine learning methods across all evaluated scenarios. Among the tested methods, ProtoNet achieved the highest performance, attaining an accuracy of 94.46% and an ROC-AUC score of 98.65%, indicating superior discriminative capability and robustness. Siamese Networks also demonstrated competitive performance under highly constrained data conditions. Furthermore, latent-space visualization, confusion matrix analysis, paired t-test statistical analysis, and ablation studies confirmed that episodic meta-learning enables the learning of highly discriminative latent representations with strong generalization capability under limited labeled data conditions. The findings highlight that FSL provides a robust and scalable framework for intelligent water quality classification in aquaculture systems, particularly in scenarios where labeled data are scarce, offering significant potential for sustainable aquaculture monitoring applications. Full article

Global forest ecosystems face growing threats from land use change, climate and weather extremes, and insects and diseases. Managing these threats is difficult due to the time, cost, and human error associated with the quality and quantity of data required for research and assessment. While conventional analytical methods are being improved constantly, they are often slow in providing information needed to respond promptly to unprecedented changes driven by both natural and anthropogenic alterations to forest ecosystems. For this reason, potential applications of artificial intelligence (AI) have attracted increasing attention in the field. Here, we examine the benefits and challenges of using AI in near-term forest health monitoring (surveillance, mostly over small scales) and discuss the need for long-term and larger-scale assessment. Abundant evidence shows that existing AI methods already facilitate the rapid collection, compilation, and synthesis of available data from diverse sources. Furthermore, emerging technologies (e.g., agentic AI) are building these capabilities into autonomous systems. However, every AI tool has advantages and limitations. With constant improvements, integrative AI-driven approaches that simultaneously deal with multiple and cross-scale interacting factors are expected to deliver actionable insights about forest health better than any single AI tool. Consequently, they can enhance decision-making processes, reduce monitoring costs, and help mitigate the impacts of forest health threats. As AI continues to evolve, it is essential to circumscribe its role in forest health monitoring. Most importantly, AI should not define what humans value regarding forest health but instead should be applied to help us evaluate data about our chosen value targets. Full article

Monitoring inland surface water quality is essential for water security, ecosystem conservation, public health, and sustainable water resource management. Although in situ measurements remain indispensable, they are often limited by high costs, restricted spatial coverage, low temporal frequency, and discontinuous monitoring networks. This study presents a systematic literature review, guided by the PRISMA 2020 framework, of empirical studies published between 2021 and 2025 on the integration of artificial intelligence (AI) and remote sensing (RS) for inland surface water quality monitoring. Searches were conducted in the Web of Science database, resulting in a final corpus of 367 peer-reviewed articles. Preliminary bibliometric characterization and qualitative content analysis were performed to identify sensors, platforms, AI paradigms, algorithms, estimated parameters, validation strategies, limitations, challenges, trends, and research gaps. The results show rapid growth in the field, with Sentinel-2 and Landsat-8 as the most recurrent sensors and multispectral data as the dominant spectral source. Machine learning approaches, especially Random Forest, Artificial Neural Networks, XGBoost, and Support Vector Machine, predominated, while deep learning, multi-source integration, hybrid models, and Explainable AI emerged as relevant trends. AI–RS integration shows strong potential to complement conventional monitoring, but persistent challenges remain regarding in situ data dependence, limited external and temporal validation, model transferability, generalization, uncertainty reporting, validation robustness, and interpretability. Full article

National Forests in the United States provide a broad range of goods and services, safeguard biological diversity, and contribute to the resilience of ecosystems, societies, and economies. Given differences in land use history and forest management approaches between National Forests and neighboring ownerships, we investigated whether they differ across a spectrum of forest health indicators, from biomass stocking to structural diversity to invasion by non-native plants. We used Nationwide Forest Inventory (NFI) plot data from within National Forest System (NFS) lands across the conterminous United States (~20,000 plots) and from within 25 km of NFS lands on other ownerships (~20,000 plots) to quantify differences in forest health indicators. Controlling for environment, geography and forest composition, we found, nationally and regionally, that NFS forest plots had significantly greater tree species and structural diversity and evenness, basal area and biomass per hectare, and seedling density than neighboring plots. They were also less invaded by non-native plants. Such forest health monitoring results are an initial step toward better understanding the status of forest health indicators for NFS forests. This is particularly important because many disturbance factors threaten the sustainability of National Forests and their capacity to provide socioeconomic and ecological benefits. Systematic monitoring of forest health across broad scales increases our understanding of how these disturbances are changing forest conditions and informs land management and policy decisions. Full article

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs). Full article

The accurate estimation of lithium-ion battery state of health (SOH) is crucial for battery monitoring, safety, and degradation assessment; however, it remains challenging because of the nonlinear nature of battery degradation, measurement noise, and variability in the battery aging trajectory. This study aims to solve these problems by proposing a hybrid attention-based BiLSTM–RF model, which combines wavelet-based signal denoising, incremental capacity analysis (ICA)-based feature extraction, stacked Bidirectional Long Short-Term Memory (BiLSTM) networks, multi-head self-attention, principal component analysis (PCA)-based feature compression, and ensemble regression using a Random Forest (RF) model with adaptive weighted fusion. The proposed framework was tested on the NASA battery datasets (B0005, B0006, B0007 and B0018) and was further validated on the Oxford Battery Degradation Dataset using leave-one-battery-out cross validation conditions. Experimental results indicated that, in general, the proposed framework outperformed the evaluated benchmark models (CNN-LSTM, BiLSTM, and RF models) in terms of the prediction error, with a minimum RMSE value of 0.0229 for NASA battery B0007 and 0.0024 for Oxford Cell3. Ablation analysis also showed that the combination of wavelet denoising, PCA compression, temporal sequence learning and ensemble regression played a role in the overall SOH estimation performance. These results show that the proposed hybrid approach is effective and stable for SOH estimation in different battery degradation trajectories under the tested experimental conditions. Full article

Urban air quality management has been playing a significant role due to its effects on public health and pollution characteristics of countries with constantly changing policies. Traditional approaches capture how much pollution is present but are unable to detect changes in the chemical character of the atmosphere, the relationships between co-emitted species, the balance of photochemical processing, and the combustion fingerprint of emission sources. This study introduces a framework that identifies and diagnoses such evolutions within the pollutants of the atmosphere. A chemistry-aware Variational Autoencoder is trained on 19 multivariate pollution features (7 raw concentrations, 5 chemical ratios, 7 temporal gradients) at London Marylebone Road (urban roadside) and North Kensington (urban background) from 2015 to 2019, and tested on 2022–2025. A four-method ensemble framework (VAE reconstruction error, reconstruction probability, Isolation Forest, and statistical Z-score) requires ≥3 agreement to identify high-confidence departed pollution states. Per-feature decomposition of the reconstruction probability diagnoses the chemical character of each departure. At the roadside site, 14.5% of post-COVID hours fall within departed states, dominated by the CO/NO_x combustion ratio (513.2) and the photostationary state proxy (391.4), chemical relationships rather than individual concentrations. This indicates that at the point of emission, London’s fleet modernisation and Ultra Low Emission Zone (ULEZ) have changed the combustion fingerprint and photochemical equilibrium. The same structural indicators are carried over during the COVID-19 lockdown; however, O₃ rises 3.2× during the pandemic period, reflecting suppressed NO titration. Conversely, at the urban background site, where the departures are driven by concentrations and boundary-layer trapping ($r=-0.659$), the combustion fingerprint of the atmosphere is invisible to detect (CO/NO_x$=-45.0$). These findings indicate that London’s emission landscape has undergone fundamental transformations over the past decade, and the consequences of ULEZ and similar interventions or greater impacts of pandemic-related events are non-homogeneously distributed across the relevant region. Full article

Wildfires pose significant threats to ecosystems, economies, and human health. The integration of remote sensing (RS), geospatial information systems (GIS), and artificial intelligence (AI) has emerged as a powerful approach for addressing wildfire-related challenges. However, existing review studies typically focus on specific wildfire tasks and lack a comprehensive synthesis of how geospatial data and supervised AI techniques interact across the full wildfire management cycle. Therefore, this study aims to provide a meta-analysis review of the integration of RS, GIS, and supervised AI methods in wildfire science. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to systematically analyze 449 peer-reviewed journal articles published between 1994 and 2024. The review examines various wildfire-related tasks, data sources, algorithmic approaches, spatial scales, performance metrics, and other aspects used in wildfire geospatial AI (GeoAI) studies. The results reveal a strong concentration of research on tasks such as burned area mapping (BAM), wildfire detection, and susceptibility mapping, while critical areas, such as fuel mapping, wildfire vulnerability, and post-fire recovery, remain underexplored. The analysis also identifies a dominant use of traditional machine learning (ML) algorithms, such as Random Forest (RF), and an increasing adoption of deep learning (DL) models, particularly convolutional neural networks (CNNs). Furthermore, the geographic distribution of studies highlights significant global disparities, with most research conducted in high-income regions, while wildfire-prone areas in developing regions remain underrepresented. The review also reveals limited adoption of advanced AI techniques, including transfer learning, transformer architectures, Geo-foundation AI models, and explainable AI (XAI). These findings provide a comprehensive synthesis of GeoAI applications in wildfire management and highlight critical methodological, geographic, and application-level gaps. Addressing these gaps through improved data accessibility, adoption of advanced AI methods, and increased research focus on underrepresented wildfire tasks and regions will be essential for developing scalable, interpretable, and globally applicable wildfire management systems. Full article

With growing interest in health and leisure, walking tourism has emerged as a significant segment of the tourism market. Coastal trails have gained prominence as attractive tourist attractions offering unique experiences that combine coastal and forest environments. Understanding the experiences of tourists using these trails is essential to their sustainability and the revitalization of nearby regions and tourist destinations. However, the sustainable management of coastal trails and the understanding of the perceptions and evaluations of tourists using them remain limited. Therefore, this study aims to analyze walking tourism experiences on coastal trails using online review data to identify tourists’ perceptions and evaluations. Three representative coastal trails in South Korea were selected as the study sites, and 21,289 reviews (including course information, titles, review content, and posting dates) were collected from Durunubi, a walking tourism application operated by the Korea Tourism Organization. The research methodology employed text mining and sentiment analysis in Python 3.12.13 and spatial analysis using GeoDa 1.22.0.20 and QGIS 3.40.11. This study explores the emotional geography of walking tourism experiences along Korean coastal trails by integrating the analysis of online review data using text mining, sentiment analysis, and spatial analysis. The analysis revealed that positive sentiments were associated with natural landscapes, while negative sentiments were associated with trail management. These emotional experiences exhibit distinct spatial clustering patterns. This finding has important implications for establishing sustainable trail management strategies. Full article

Dissolved oxygen (DO) is a critical indicator for assessing marine ecological health and hypoxia risk. Most existing DO prediction studies rely on time-series forecasting models, which require continuous temporal observations and are often unreliable in practical marine monitoring scenarios due to sparse sampling, missing records, and heterogeneous measurement conditions. To address this limitation, this paper investigates the problem of non-temporal DO prediction, aiming to learn a direct nonlinear mapping between environmental drivers and DO concentration. To explicitly model nonlinear pairwise interaction effects between environmental variables, we propose a Factor-Interaction Neural Network (FINN), which decomposes DO estimation into main effects and structured pairwise interaction effects. This interaction-driven design enhances both representation capacity and interpretability compared with conventional multilayer perceptrons. Furthermore, we develop a physics-informed extension, termed PI-FINN, by incorporating oceanographic-consistent regularization priors that reflect key DO formation mechanisms, including temperature-related solubility behavior, depth-wise smoothness associated with stratification, and chlorophyll-driven biological oxygen production tendencies. To evaluate the physical plausibility of model predictions beyond standard accuracy metrics, we introduce a physics-consistency assessment protocol based on Physics Consistency Violation Rate (PCVR) and its robust variant, and further analyze their convergence stability under different driver-weight configurations. Extensive experiments on a real-world marine dataset demonstrate that FINN achieves competitive predictive accuracy compared with strong machine learning baselines (e.g., SVR, Random Forest, and XGBoost), while the proposed physics-informed design mainly improves the physical consistency, robustness, and interpretability of DO estimation under heterogeneous environmental regimes, although it does not necessarily guarantee superior RMSE or MAE performance compared with purely data-driven models. Specifically, FINN achieves an RMSE of 0.3130, an $R^2$ of 0.9831, and a PCVR of 0.4826 on a dataset composed of key environmental variables, including depth, temperature, salinity, and chlorophyll-a, collected under sparse and irregular sampling conditions. Ablation studies confirm the effectiveness of both factor-interaction modeling and physics-guided regularization components. Overall, the proposed framework further provides a reliable tool for sustainable environmental monitoring by enabling physically consistent dissolved oxygen prediction under sparse observational conditions. Such capability is critical for supporting sustainable water resource management, hypoxia risk assessment, and long-term ecological protection. Full article

The southern pine beetle (SPB) is a serious pest of pine forests from Central America to the eastern United States, with a recent range expansion into the northeastern United States. Efforts to detect and monitor SPB activity began in 1960 as part of an overall integrated pest management system to limit its impact to southern pine forests. The ubiquity of SPB’s pine hosts in the southern United States, in the form of plantations and natural mixed stands, along with the regular occurrence of SPB outbreaks over a vast region, makes SPB a leading driver of overall forest health across this region. We review the past and current methodology for collecting SPB-related pine mortality and outbreak data using aerial and ground survey techniques and remote sensing via satellite imagery. We show how historical and ongoing measurements of SPB abundance, from pheromone-baited traps and aerial surveys, are used to forecast near-term probabilities of outbreaks with a statistical model (actualized through a public URL) that captures the natural tendency of SPB populations to be very high or very low. Insect forecasts can also be combined with maps of the host distributions to generate predictions of short-term regional risks and longer-term tree mortality forecasts via the US Forest Service’ National Insect and Disease Risk Map (NIDRM). Because the measurements of insect abundance and impact outcomes have become part of continuing forest management operations, statistical models can continue to be improved and there is self-reinforcing feedback between models and management. Improved understanding and monitoring of prominent insect pests that impact abundant tree species is a pathway to managing forest health more broadly. Full article