Search Results (945)

In recent years, the availability of automated change detection algorithms in Google Earth Engine has permitted the cloud-based processing of large quantities of satellite imagery. Models such as the Continuous Change Detection and Classification (CCDC), CCDC-Spectral Mixture Analysis (CCDC-SMA), and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) allow users to exploit decades of Earth Observations (EOs), leveraging the Landsat archive and data from other sensors to detect disturbances in forest ecosystems. Despite the wide adoption of these methods, robust documentation, and a growing community of users, little research has systematically detailed their tuning process in mangrove environments. This work aims to identify the best practices for applying these models to monitor changes within mangrove forest cover, which has been declining gradually in Belize the last several decades. Partnering directly with the Belizean Forest Department, our team developed a replicable, efficient methodology to annually update the country’s mangrove extent, employing EO-based change detection. We ran a series of model variations in both CCDC-SMA and LandTrendr to identify the parameterizations best suited to identifying change in Belizean mangroves. Applying the best performing model run to the starting 2017 mangrove extent, we estimated a total loss of 540 hectares in mangrove coverage by 2024. Overall accuracy across thirty variations in model runs of LandTrendr and CCDC-SMA ranged from 0.67 to 0.75. While CCDC-SMA generally detected more disturbances and had higher precision for true changes, LandTrendr runs tended to have higher recall. Our results suggest LandTrendr offered more flexibility in balancing precision and recall for true changes compared to CCDC-SMA, due to its greater variety of adjustable parameters. Full article

Ensuring grid stability in the transition to renewable energy sources requires accurate power demand forecasting. This study addresses the need for precise forecasting by differentiating among industrial, commercial, and residential consumers through customer clusterisation, tailoring the forecasting models to capture the unique consumption patterns of each group. Feature selection incorporated temporal, socio-economic, and weather-related data obtained from the Copernicus Earth Observation (EO) program. A variety of AI and machine learning algorithms for short-term load forecasting (STLF) and very-short-term load forecasting (VSTLF) are explored and compared, determining the most effective approaches. With all that, the main contribution of this work are the new forecasting approaches proposed, which have demonstrated superior performance compared to simpler models, both for STLF and VSTLF, highlighting the importance of customized forecasting strategies for different consumer groups and demonstrating the impact of incorporating detailed weather data on forecasting accuracy. These advancements contribute to more reliable power demand predictions, with our novel forecasting approaches reducing the Mean Absolute Percentage Error (MAPE) by up to 1–3% for industrial and 1–10% for commercial consumers compared to baseline models, thereby supporting grid stability. Full article

Epilepsy manifests as recurrent spontaneous seizures associated with irregular brain activity. Recognizing the limitations of conventional antiepileptic treatments, we explored the therapeutic potential of essential oils (EOs) derived from Greek aromatic plants (Mentha pulegium, Mentha spicata wild, Mentha piperita, Lavandula angustifolia and Origanum Dictamnus). Specifically, we explored their radical scavenging capacity (DPPH), as well as their antioxidant (AOP and MDA levels) and neuroprotective effect in a PTZ-induced epilepsy Balb/c mice model (animals were pretreated with EOs prior to PTZ treatment). Our results indicated that Mentha piperita emerges as the most promising EO, demonstrating strong antioxidant activity and the highest radical scavenging ability (IC₅₀ = 1.9 mg/mL). Mentha pulegium also exhibited considerable antioxidant potential, demonstrating the strongest effect in the AOP assay when administered prior to PTZ treatment. Furthermore, Origanum dictamnus exhibited the strongest potential to attenuate MDA formation in the presence of PTZ. Finally, immunohistochemistry indicated a trend of neuronal preservation in animals pretreated with EOs prior to PTZ, with Mentha piperita demonstrating the most significant effect. Based on these findings, we suggest that certain EOs possess significant antioxidant and neuroprotective properties. Further research is warranted to validate these results and elucidate the active ingredients responsible for the observed properties. Full article

The growing volume of decommissioned wind turbine blades (WTBs) poses substantial challenges for end-of-life (EoL) material management, particularly within the composite repurposing and recycling strategies. This study investigates the repurposing of EoL WTB segments in a full-scale demonstrator for a photovoltaic (PV) floating platform. The design process is supported by a calibrated numerical model replicating the structure’s behaviour under representative operating conditions. The prototype reached Technology Readiness Level 6 (TRL 6) through laboratory-scale wave basin testing, under irregular wave conditions with heights up to 0.22 m. Structural assessment validates deformation limits and identifies critical zones using composite failure criteria. A comparison between two configurations underscores the importance of load continuity and effective load distribution. Additionally, a life cycle assessment (LCA) evaluates environmental impact of the repurposed solution. Results indicate that the demonstrator’s footprint is comparable to those of conventional PV-floating installations reported in the literature. Furthermore, overall sustainability can be significantly enhanced by reducing transport distances associated with repurposed components. The findings support the structural feasibility and environmental value of second-life applications for composite WTB segments, offering a circular and scalable pathway for their integration into aquatic infrastructures. Full article

Essential oils (EOs) from the leaves of Origanum vulgare subsp. virens (Hoffmanns. & Link) Bonnier & Layens, representing three chemotypes—thymol-rich, carvacrol-rich, and a mixed thymol–carvacrol type—were chemically characterized and comparatively assessed for their antifungal, insecticidal, and enzyme-inhibitory activities. This integrated approach provides a comparative assessment of all three chemotypes across multiple biological models, including phytopathogenic fungi, insect bioassays, and key enzyme targets. All EOs displayed antifungal activity for the tested phytopathogenic fungi (Alternaria alternata, Botrytis cinerea, and Fusarium oxysporum) at concentrations above 0.5 mg/mL, with the thymol-rich chemotype showing the highest activity. The minimum inhibition concentration for Oidium farinosum conidial growth was determined and found to be similar for thymol and carvacrol chemotypes and lower for the terpene mixture. Insect control activity was evaluated by an antifeeding assay, where carvacrol and especially thymol chemotypes can be classified as feeding deterrents. EOs and standards revealed a weak toxicity against Ceratitis capitata, with less than 20% mortality at a concentration of 50 mg/mL, and both chemotypes were found to be ineffective in preventing egg deposition. The acetylcholinesterase (AChE) inhibition assay revealed that carvacrol had the greatest inhibitory effect on AChE, followed by EOs, and, finally, thymol. Regarding the α- and β-glucosidase (α- and β-GLU) inhibitory assays, thymol had the strongest inhibitory effect on α-GLU, while plant β-GLU was not inhibited by the standards or OEs. Full article

Tight gas reservoirs, as important unconventional natural gas resources, face low recovery rates due to low porosity, low permeability, and strong heterogeneity. CO₂ Storage with Enhanced Gas Recovery (CSEGR) technology combines CO₂ geological storage with natural gas development, providing both economic and environmental benefits. However, the main controlling factors and influence mechanisms remain unclear. This study utilized the PR-EOS to investigate CH₄, CO₂, and natural gas physical properties, established a numerical simulation model considering CO₂ dissolution and geochemical reactions, and explored the influence of injection scheme, injection rate, production rate, and shut-in condition on CO₂ enhanced recovery and storage effectiveness through orthogonal design. Results show that CO₂ exhibits significant differences in compressibility factor, density, and viscosity compared to natural gas, enabling piston-like displacement. Intermittent injection slightly outperforms continuous injection in recovery enhancement, while continuous injection provides greater CO₂ storage capacity. The ranking of the significance of different influencing factors for enhanced oil recovery is as follows: injection rate > production rate > injection scheme > shut-in condition. For the effect of geological storage of CO₂, it is as follows: injection rate > injection scheme > production rate > shut-in condition. During gas injection, supercritical, ionic, and dissolved CO₂ continuously increase while mineral CO₂ decreases, with storage mechanisms dominated by structural and residual trapping. The study provides scientific basis for optimizing CO₂ flooding strategies in tight gas reservoirs. Full article

The use of cover crops is one of the most effective practices for maintaining, or even improving, the carbon balance of agricultural soils, while offering various ecosystem benefits. However, replacing bare soil with cover crops can increase transpiration and potentially reduce the water available for subsequent cash crops. The study takes place in southwestern France where it is essential to strike a balance between carbon storage and water availability, and where agroecological practices are encouraged and water resources are limited and expected to diminish with climate change. In this study, estimates of cover crop biomass production, as well as of the components of the water and carbon cycles, are carried out using a hybrid approach, AgriCarbon-EO, combining modeling, remote sensing, and assimilation, with quantification of target variables and their uncertainties at decametric resolution. The SAFYE-CO₂ agrometeorological model used in AgriCarbon-EO is calibrated to represent cover crops development, and simulated variables are compared with CO₂ fluxes and evapotranspiration measured by eddy covariance (for NEE, R² = 0.57, RMSE = 0.97 gC·m⁻²; for ETR, R² = 0.42, RMSE = 0.87 mm), as well as to an extensive above-ground biomass dataset (R² = 0.71, RMSE = 93.3 g·m⁻²). Knowing the local performance of the approach, a large-scale, decametric-resolution modeling exercise was carried out to simulate winter cover crops in southwestern France, over five contrasting fallow periods. The significant variability in cover crop phenology and above-ground biomass was characterized, and estimates of the amount of humified carbon added to the soil by cover crops were quantified at the pixel level. With amounts ranging from 40 to 130 gC·m⁻² for most of the considered pixels, these new SOC values show clear trends as a function of cumulative evapotranspiration. However, the impact of cover crops on soil water content appears to be minimal due to spring precipitation. Full article

The successful integration of Terrestrial and Non-Terrestrial Networks (T/NTNs) in 6G is poised to revolutionize demanding domains like Earth Observation (EO) and Intelligent Transportation Systems (ITSs). Still, it requires Distributed Machine Learning (DML) frameworks that are scalable, private, and efficient. Existing methods, such as Federated Learning (FL) and Split Learning (SL), face critical limitations in terms of client computation burden and latency. To address these challenges, this paper proposes a novel hierarchical DML paradigm. We first introduce Federated Split Transfer Learning (FSTL), a foundational framework that synergizes FL, SL, and Transfer Learning (TL) to enable efficient, privacy-preserving learning within a single client group. We then extend this concept to the Generalized FSTL (GFSTL) framework, a scalable, multi-group architecture designed for complex and large-scale networks. GFSTL orchestrates parallel training across multiple client groups managed by intermediate servers (RSUs/HAPs) and aggregates them at a higher-level central server, significantly enhancing performance. We apply this framework to a unified T/NTN architecture that seamlessly integrates vehicular, aerial, and satellite assets, enabling advanced applications in 6G ITS and EO. Comprehensive simulations using the YOLOv5 model on the Cityscapes dataset validate our approach. The results show that GFSTL not only achieves faster convergence and higher detection accuracy but also substantially reduces communication overhead compared to baseline FL, and critically, both detection accuracy and end-to-end latency remain essentially invariant as the number of participating users grows, making GFSTL especially well suited for large-scale heterogeneous 6G ITS deployments. We also provide a formal latency decomposition and analysis that explains this scaling behavior. This work establishes GFSTL as a robust and practical solution for enabling the intelligent, connected, and resilient ecosystems required for next-generation transportation and environmental monitoring. Full article

Background/Objectives: This study aimed to investigate the traditionally claimed anti-inflammatory effect of essential oil (EO) derived from pine green cones per se and after encapsulation into liposomes, which is expected to enhance its bioactivity and stability. Methods: The chemical profiling of EO was conducted using GC/GC-MS. The physico-chemical characterization of the liposomal formulation (LEO) included encapsulation efficiency, FTIR spectroscopy, and AFM imaging. Additionally, parameters such as mean particle diameter, polydispersity index, zeta potential, pH, and electrical conductivity were evaluated and reassessed after 30 days and 1 year to determine formulation stability. The in vivo anti-inflammatory effect of the EO and LEO was examined using a carrageenan-induced rat paw edema model. Results: The Pinus halepensis EO contained 14 components, mainly, α-pinene, myrcene, and (E)-caryophyllene. Encapsulation efficiency was 97.35%. AFM analyses confirmed the nanoscale dimensions and spherical shape of liposomes, while FTIR indicated successful encapsulation through overlapping functional groups. The droplet size of blank liposomes (L) ranged from 197.4 to 217 nm, while adding the EO decreased the droplet size and electrical conductivity. The polydispersity index (PDI) remained below 0.2. The zeta potential of the liposomes was between −35.61 and −49.43 mV, while the pH value was in the range of 4.35 to 5.01. These results indicate satisfactory stability across repeated measurements. Administration of LEO significantly inhibited paw edema relative to the controls, with a percentage inhibition of approximately 69%, which does not significantly differ from the effect of hydrocortisone, which was used as a positive control. Conclusions: This is the first study to report liposomal encapsulation and in vivo anti-inflammatory activity of an EO derived specifically from green cones of P. halepensis. Our findings demonstrate that EO-loaded liposomes exhibited favorable physico-chemical properties and notable anti-inflammatory activity, comparable to that of hydrocortisone. These results support their potential application in the development of effective topical anti-inflammatory formulations. Full article

Essential oils (EOs) are natural mixtures of volatile compounds characterized by beneficial pharmacological effects. The repeated inhalation of EOs in olfactory training (OT) has been demonstrated to improve the sense of smell in patients with olfactory deficits. We conducted a conjunct evaluation of the chemical composition, sensory profile, and bioactivity in cell models of commercial EOs of rose (EO1), eucalyptus (EO2), lemon (EO3), and clove (EO4) used for OT (StimuScent^®, Dos Medical, Sense Trading BV, Groningen, The Netherlands). Citronellol, 1,8-cineole, limonene, and eugenol emerged as the most abundant volatile compounds in EO1, EO2, EO3, and EO4, respectively, by GC-MS analysis. Some differences emerged (using a Likert-type scale) in the perception of EO’s odor dimensions (pleasantness, intensity, and familiarity in subjects with hyposmia (n = 8) compared to controls (n = 22). Cytotoxicity assays (24 h of incubation) demonstrated the anticancer effects of EOs (5–100 μg/mL) on SH-SY5Y neuroblastoma cells (the order of potency was EO3 > EO4 > EO2 > EO1), while all EOs showed lower effects on the viability/morphology of human skin HaCaT keratinocytes. SH-SY5Y cancer cells grown for six days with different EOs (at 50 μg/mL) showed evident signs of toxicity and apoptosis. Marked changes in cell morphology (structure/number of processes) were evidenced in clove EO-treated cells. EO’s sensory properties/bioactivity were also related to the in silico physicochemical/pharmacokinetic properties of the main EO components. Our results provide new insights into a more targeted EO application for OT. Full article

This study, inspired by the impact resistance of the pistol shrimp’s predatory claw, investigates the design and optimization of bionic energy absorption structures. Four types of bionic hierarchical porous metamaterial lattice structures with a negative Poisson’s ratio were developed based on the microstructure of the pistol shrimp’s fixed claw. These structures were validated through finite element models and quasi-static compression tests. Results showed that each structure exhibited distinct advantages and shortcomings in specific evaluation indices. To address these limitations, four new bionic structures were designed by coupling the characteristics of the original structures. The coupled structures demonstrated a superior balance across various performance indicators, with the EOS (Eight pillars Orthogonal with Side connectors on square frame) structure showing the most promising results. To further enhance the EOS structure, a parametric study was conducted on the distance d from the edge line to the curve vertex and the length-to-width ratio y of the negative Poisson’s ratio structure beam. A fifth-order polynomial surrogate model was constructed to predict the Specific Energy Absorption (SEA), Crush Force Efficiency (CFE), and Undulation of Load-Carrying fluctuation (ULC) of the EOS structure. A multi-objective genetic algorithm was employed to optimize these three key performance indicators, achieving improvements of 1.98% in SEA, 2.42% in CFE, and 2.05% in ULC. This study provides a theoretical basis for the development of high-performance biomimetic energy absorption structures and demonstrates the effectiveness of coupling design with optimization algorithms to enhance structural performance. Full article

The karst terrain of Guangxi, China, characterized by steep slopes and thin residual soils, is highly vulnerable to rainfall-induced shallow landslides. Timely and accurate displacement forecasting is critical for early warning and risk mitigation. However, most existing systems depend on centralized computation, leading to latency and reduced responsiveness. Moreover, conventional forecasting models are often too computationally intensive for edge devices with limited processing resources. To address these constraints, we present EoML-SlideNet, a lightweight forecasting framework designed for resource-limited hardware. It decomposes displacement and triggers into trend and periodic components, then applies the Dual-Band Lasso-Enhanced Latent Variable (DBLE–LV) module to select compact, interpretable features via cross-correlation, LASSO, and VIF screening. A small autoregressive model predicts the trend, while a lightweight neural network captures periodic fluctuations. Their outputs are combined to estimate displacement. All models were evaluated on a single CPU-only workstation to ensure fair comparison. This study introduces floating-point operations (FLOPs), alongside runtime, as practical evaluation metrics for landslide displacement prediction models. A site-specific multi-sensor dataset was developed to monitor rainfall-triggered landslide behavior in the karst terrain of Guangxi. The experimental results show that EoML-SlideNet achieves 2–4 times lower MAE/RMSE than the most accurate deep learning and the lightest baseline models, while offering 3–30 times faster inference. These results demonstrate that low-complexity models can match or surpass the accuracy of deep networks while achieving latency and FLOP levels suitable for edge deployment without dependence on remote servers. Full article

The problem of end-of-life (EoL) fibre-reinforced polymer (FRP) wind turbine blades (WTBs) poses a growing challenge due to the absence of an integrated circular value chain currently available on the market. A key barrier is the information gap between the EoL condition of WTB components and their second-life application requirements. This study addresses this question by focusing on the spar cap, which is an internal structural component with high repurposing potential. A framework has been developed to determine the as-received mechanical properties of spar caps from different EoL WTB models, targeting repurpose in the construction sector. The experimental programme encompasses fibre architecture assessment, calcination processes and mechanical tests in both longitudinal and transverse directions of three different WTB models. Results suggest that the spar caps appear to retain their strength and stiffness, with no evidence of degradation from previous service life. However, notable variation in properties is observed. To account for this, a prediction tool is proposed to estimate the as-received mechanical properties based on practically accessible parameters, thereby supporting decision-making. The results of this study contribute to enabling the repurposing of EoL spar cap beams from the wind energy sector for applications in the construction sector. Full article

Background: Accurate differentiation of Parkinson’s disease (PD) from healthy aging is crucial for timely intervention and effective management. Postural sway abnormalities are prominent motor features of PD. Quantitative stabilometry and machine learning (ML) offer a promising avenue for developing objective markers to support the diagnostic process. This study aimed to develop and validate high-performance ML models to classify individuals with PD and age-matched healthy older adults (HOAs) using a comprehensive set of stabilometric parameters. Methods: Thirty-seven HOAs (mean age 70 ± 6.8 years) and 26 individuals with idiopathic PD (Hoehn and Yahr stages 2–3, on medication; mean age 66 years ± 2.9 years), all aged 60–80 years, participated. Stabilometric data were collected using a force platform during quiet stance under eyes-open (EO) and eyes-closed (EC) conditions, from which 34 parameters reflecting the time- and frequency-domain characteristics of center-of-pressure (COP) sway were extracted. After data preprocessing, including mean imputation for missing values and feature scaling, three ML classifiers (Random Forest, Gradient Boosting, and Support Vector Machine) were hyperparameter-tuned using GridSearchCV with three-fold cross-validation. An ensemble voting classifier (soft voting) was constructed from these tuned models. Model performance was rigorously evaluated using 15 iterations of stratified train–test splits (70% train and 30% test) and an additional bootstrap procedure of 1000 iterations to derive reliable 95% confidence intervals (CIs). Results: Our optimized ensemble voting classifier achieved excellent discriminative power, distinguishing PD from HOAs with a mean accuracy of 0.91 (95% CI: 0.81–1.00) and a mean Area Under the ROC Curve (AUC ROC) of 0.97 (95% CI: 0.92–1.00). Importantly, feature analysis revealed that anteroposterior sway velocity with eyes open (V-AP) and total sway path with eyes closed (TOD_EC, calculated using COP displacement vectors from its mean position) are the most robust and non-invasive biomarkers for differentiating the groups. Conclusions: An ensemble ML approach leveraging stabilometric features provides a highly accurate, non-invasive method to distinguish PD from healthy aging and may augment clinical assessment and monitoring. Full article

The growing concern about climate change and increased carbon emissions has promoted the electric vehicle market. Lithium-Ion Batteries (LIBs) are now the prevailing technology in electromobility, and large amounts will soon reach their end-of-life (EoL). Most counties have not designed sustainable reverse logistics networks to collect, remanufacture and recycle EoL electric vehicle batteries (EVBs). This study is focused on estimating the future EoL LIBs generation through dynamic material flow analysis using a three parameter Weibull distribution function under two scenarios for battery lifetime and then designing a reverse logistics network for the region of Attica (Greece), based on a generalizable modeling framework, to handle the discarded batteries up to 2040. The methodology considers three different battery handling strategies such as recycling, remanufacturing, and disposal. According to the estimated LIB waste generation in Attica, the designed network would annually manage between 5300 and 9600 tons of EoL EVBs by 2040. The optimal location for the collection and recycling centers considers fixed costs, processing costs, transportation costs, carbon emission tax and the number of EoL EVBs. The economic feasibility of the network is also examined through projected revenues from the sale of remanufactured batteries and recovered materials. The resulting discounted payback period ranges from 6.7 to 8.6 years, indicating strong financial viability. This research underscores the importance of circular economy principles and the management of EoL LIBs, which is a prerequisite for the sustainable promotion of the electric vehicle industry. Full article