Search Results (800)

Detecting marine oil spills is vital for protecting the marine environment, ensuring maritime traffic safety, supporting marine development, and enabling effective emergency response. The dual-polarimetric (DP) synthetic aperture radar (SAR) system represents an evolution from single to full polarization (FP), which has become an essential tool for oil spill detection with the growing availability of open-source and shared datasets. Recent research has focused on enhancing DP information structures to better exploit this data. This study introduces improved DP models’ structure with modified the scattering vector coefficients to ensure consistency with the corresponding components of the FP system, enabling comprehensive comparison and analysis with traditional DP structure, includes theoretical and quantitative evaluations of simulated data from FP system, as well as validation using real DP scenarios. The results showed the following: (1) The polarimetric entropy H_L obtained through the improved DP scattering matrix C_L can achieve higher information consistency results closely aligns with FP system and better performance, compared to the typical two DP scattering structures. (2) For multiple polarimetric features from DP scattering matrix (both traditional feature and combination feature), the improved DP scattering matrix C_L can be used for oil spill extraction effectively with prominent results. (3) For oil spill extraction, the polarimetric features-based C_L tend to have relatively high contribution, especially the H_A feature combination, leading to substantial gains in improved classification performance. This approach not only enriches the structural information of the DP system under VV–VH mode but also improves oil spill identification by integrating multi-structured DP features. Furthermore, it offers a practical alternative when FP data are unavailable. Full article

Selenium is an important trace element with certain antioxidant effects. Nano-selenium, as a novel selenium source, has the advantages of strong biological activity, high absorption efficiency, and low toxicity. The aim of the present study was to compare the protective effects of sodium selenite and nano-selenium on intestinal oxidative stress induced by hydrogen peroxide (H₂O₂) in mice. A total of 60 female mice were randomly divided into 6 groups with 10 replicates per group and 1 mouse per replicate (n = 10). The first three groups were as follows: the Control group (C), fed with basal diet; the sodium selenite group (SS), basal diet + 0.3 mg·kg⁻¹ sodium selenite; and the nano-selenium group (NS), basal diet + 0.3 mg·kg⁻¹ nano-selenium. The latter three groups (CH, SSH, NSH) were fed the same diet as the former three groups, but the last 10 days of the experiment were fed with drinking water containing 0.3% H₂O₂ to induce oxidative stress. The results showed that under normal conditions, the supplementation with sodium selenite or nano-selenium decreased the spleen index of mice; sodium selenate up-regulates GPX3 expression in the ileum, and increases T-SOD in the colon of mice; and nano-selenium up-regulated GPX1 expression but decreased T-AOC in the jejunum. After drinking water treated with H₂O₂, H₂O₂ increased the expression of intestinal inflammatory factors and selenium proteins, such as IL-1β and SOD in jejunum, IL-1β, NF-κB, IL-10, TXNRD1, TXNRD2, GPX1, GPX3, GPX4, and CAT in ileum, and IL-1β and SOD in colon. At the antioxidant level, H₂O₂ decreased T-AOC in the jejunum. In the H₂O₂ treatment, sodium selenite and nano-selenium increased the ratio of VH to CD (VH/CD) in jejunum; sodium selenite up-regulated the expression of TXNRD1 in jejunum, down-regulated the expression of GPX3 in ileum, at the antioxidant level, decreased the T-SOD and T-AOC in colon, and increased the content of MDA in ileum; and nano-selenium down-regulated the expression of TXNRD1 in colon. At the same time, the expression of IL-1β, NF-κB, IL-10, TXNRD1, TXNRD2, GPX1, GPX4, and CAT can be restored to normal levels by selenium supplementation. According to the results, drinking H₂O₂ induced intestinal oxidative stress in mice to a certain extent, and selenium supplementation mitigated the destructive effect of H₂O₂ on the intestinal morphology of mice jejunum and restored the level of related inflammatory factors, and had a positive effect on antioxidants. Full article

Linpan ecosystems, distinct to western Sichuan, China, are integral to regional biodiversity and carbon cycling. However, comprehensive biomass estimation for these systems has not been thoroughly investigated. This study seeks to fill this gap by enhancing the accuracy and precision of biomass estimation in these ecologically vital landscapes through the application of multi-source remote sensing techniques, specifically by integrating the strengths of optical and radar remote sensing data. The focus of this research is on the forest biomass of Linpan, encompassing the tree layer, which includes the trunk, branches, leaves, and underground roots. Specifically, the research focused on the Linpan ecosystems in the Wenjiang District of western Sichuan, utilizing an integration of Sentinel-1 SAR, Sentinel-2 multispectral, and GF-2 high-resolution data for multi-source remote sensing-based biomass estimation. Through the preprocessing of these data, Pearson correlation analysis was conducted to identify variables significantly correlated with the forest biomass as determined by field surveys. Ultimately, 19 key modeling factors were selected, including band information, vegetation indices, texture features, and phenological characteristics. Subsequently, three algorithms—multiple stepwise regression (MSR), support vector machine (SVM), and random forest (RF)—were employed to model biomass across mixed-type, deciduous broadleaved, evergreen broadleaved, and bamboo Linpan. The key findings include the following: (1) Sentinel-2 spectral data and Sentinel-1 VH backscatter coefficients during the summer, combined with vegetation indices and texture features, were critical predictors, while phenological indices exhibited unique correlations with biomass. (2) Biomass displayed a marked north–south gradient, characterized by higher values in the south and lower values in the north, with a mean value of 161.97 t ha⁻¹, driven by dominant tree species distribution and management intensity. (3) The RF model demonstrated optimal performance in mixed-type Linpan (R² = 0.768), whereas the SVM was more suitable for bamboo Linpan (R² = 0.892). The research suggests that integrating multi-source remote sensing data significantly enhances Linpan biomass estimation accuracy, offering a robust framework to improve estimation precision. Full article

Remote sensing of moving targets, particularly pedestrians on the road, is crucial for advanced driver assistance systems. However, pedestrian detection using the radar system remains an ongoing challenge due to the radar cross section (RCS) of pedestrians being much smaller than that of the clutter. Existing radar systems and pedestrian detection methods predominantly rely on the single-polarization radar, while research on the fully polarized radar for pedestrian detection is relatively limited. In this paper, the L-band fully polarimetric radar system is developed for pedestrian detection, and based on the full polarized radar echo HH, HV, VH, and VV, a novel clutter suppression method is proposed, which integrates the optimal polarization states of antennas and optimal scattering characteristics of pedestrians. Moreover, the field experiment has been conducted, and the results demonstrate that the signal-to-clutter-plus-noise ratio (SCNR) of the total power signal of full-polarization echoes is higher than that of single-polarization echoes, and the proposed clutter suppression method is able to reduce the non-stationary clutter and the interference signal generated by the multipath effect, thereby improving the SCNR. Furthermore, the OTSU algorithm is employed to detect pedestrian targets using radar data before and after clutter suppression, and the results demonstrate that the proposed method yields superior detection performance. These findings justify the potential of fully polarimetric radar in enhancing pedestrian detection. Full article

The traditional recycling process of spent lithium-ion battery(LIB) with high Mn content faces the defects of high cost of neutralization and precipitation, poor economics of Mn extraction, and serious Li loss. Therefore, this paper introduces a comprehensive hydrometallurgical method for extracting valuable metals from high-Mn spent LIB. Particularly, directional precipitation of Mn was achieved by utilizing its redox properties, and shot-process extraction and enrichment of Li was realized by using the extractant HBL121. In a sulfuric acid system, control of the oxidant dosage to 0.8% resulted in high leaching efficiencies for Li, Ni, Co, and Mn, with values of 96.58%, 96.13%, 95.22%, and 94.24%, respectively, under optimal conditions which were C(H₂SO₄) of 3.5 mol/L, V(H₂O₂) of 0.8% (v/v), L/S of 10:1, temperature of 60 °C, and time of 60 min. Subsequently, the addition of KMnO₄ dosage (K_p/K_t) in a ratio of 1:1 resulted in the precipitation of 98.47% of Mn as MnO₂, with Ni and Li precipitation efficiencies of 0.2% and 0.1%, respectively. Cascade extraction of Ni and Co was reached by using Cyanex272 extractant from the solution after Mn precipitation. At an organic-to-aqueous phase ratio (O/A) of 1:5, the Co extraction efficiency reached 98.68%, whereas the loss efficiency of Ni was 5.53%, and Li was less than 0.1%. Adjusting the O/A to 1:1 increased the Ni extraction efficiency to 89.99% and Li loss to 8.95%. Finally, the HBL121 extractant was utilized to extract Li from the Li-rich solution, achieving 95.08% extraction efficiency. The Li was stripped with 2 mol/L H₂SO₄ from the load organic phase, realizing a Li concentration of 11.44 g/L. Thus, this process facilitates the comprehensive and efficient recovery of valuable metals such as Li, Ni, Co, and Mn from spent high-Mn LIB. Full article

Addressing limitations in remote sensing irrigation monitoring (insufficient resolution, single-source constraints, poor terrain adaptability), this study developed a high-precision identification framework for Jianping County, China, a semi-arid region. We integrated Sentinel-1 SAR (VV/VH), Sentinel-2 multispectral, and MOD11A1 land surface temperature data. Savitzky–Golay (S-G) filtering reconstructed time-series datasets for NDVI, SAVI, TVDI, and VV/VH backscatter coefficients. Irrigation mapping employed random forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms. Key results demonstrate the following. (1) RF achieved superior performance with overall accuracies of 91.00% (2022), 88.33% (2023), and 87.78% (2024), and Kappa coefficients of 86.37%, 80.96%, and 80.40%, showing minimal deviation (0.66–3.44%) from statistical data; (2) SAVI and VH exhibited high irrigation sensitivity, with peak differences between irrigated/non-irrigated areas reaching 0.48 units (SAVI, July–August) and 2.78 dB (VH); (3) cropland extraction accuracy showed <3% discrepancy versus governmental statistics. The “Multi-temporal Feature Fusion + S-G Filtering + RF Optimization” framework provides an effective solution for precision irrigation monitoring in complex semi-arid environments. Full article

Forest fires, intensified by climate change, threaten tropical ecosystems by accelerating biodiversity loss, releasing carbon emissions, and altering hydrological cycles. Continuous detection of fire-induced forest loss is therefore critical. However, commonly used optical-based methods often face limitations, particularly due to cloud cover and coarse spatial resolution. This study explores the use of C-band Sentinel-1 Synthetic Aperture Radar (SAR) time series, combined with Bayesian Online Changepoint Detection (BOCD), for detecting and continuously monitoring fire-induced vegetation loss in forested areas. Three BOCD variants are evaluated: two single-polarization approaches individually using VV and VH reflectivities, and a dual-polarization approach ($pol$-BOCD) integrating both channels. The analysis focuses on a fire-affected area in Baixo Uraim (Paragominas, Brazil), supported by field-validated reference data. BOCD performance is compared against widely used optical products, including MODIS and VIIRS active fire and burned area data, as well as Sentinel-2-based difference Normalized Burn Ratio (dNBR) assessments. Results indicate that $pol$-BOCD achieves spatial accuracy comparable to dNBR (88.2% agreement), while enabling detections within a delay of three Sentinel-1 acquisitions. These findings highlight the potential of SAR-based BOCD for rapid, cloud-independent monitoring. While SAR enables continuous detection regardless of atmospheric conditions, optical imagery remains essential for characterizing the type and severity of change. Full article

The effects of varying dietary metabolizable energy (ME) and crude protein (CP) levels, along with their interactive effects, on the apparent nutrient metabolism, development of digestive organs, intestinal morphology, and microbiota composition in Jingyuan chickens during the growing phase were evaluated. A total of 540 seven-week-old male Jingyuan chickens were randomly assigned to nine groups, with six replicates per group and 10 chickens per replicate. The trial lasted for 11 weeks. A 3 × 3 factorial design was adopted, comprising three levels of ME, namely, low (11.28 MJ/kg, LE group), medium (11.70 MJ/kg, ME group), and high (12.12 MJ/kg, HE group) and three levels of CP, namely, low (14.00%, LP group), medium (15.50%, MP group), and high (17.00%, HP group). The levels of ME and CP, along with their interactions, had significant effects on the average daily gain (ADG), average daily feed intake, feed conversion ratio (FCR), apparent metabolizable rate of CP, gizzard weight, duodenal lengths, jejunal villus height (VH), crypt depth (CD), and muscle layer thickness (MLT) (p < 0.05). The combination of medium level ME (11.70 MJ/kg) and medium level CP (15.50%) (MEMP group) exhibited the best performance, with the highest ADG and the lowest FCR (p < 0.05). Moreover, this group showed significantly higher duodenal length, jejunal CD, VH/CD and MLT compared with the other groups (p < 0.05). Dietary ME and CP levels greatly influenced cecal microbiota composition. Chickens in the MEMP group exhibited an increased abundance of Erysipelotrichaceae, Syntrophomonadaceae, Akkermansia, and Clostridia_vadinBB60_group, and there was an obvious decrease in the relative abundance of Desulfobacterota (p < 0.05). This study demonstrated that dietary ME and CP levels, along with their interactions, could significantly influence the growth performance, apparent nutrient metabolism, and intestinal development of Jingyuan chickens during the growing phase. Dietary ME and CP levels modulated the cecal microbiota composition, potentially inhibiting the abundance of harmful bacteria Desulfobacterota, while enriching the abundance of beneficial bacteria, thereby enhancing gut development and nutrient absorption. The combination of medium-level ME and CP (11.70 MJ/kg ME, 15.50% CP) demonstrated the most favorable outcomes in our study. Full article

Crop classification plays a vital role in acquiring the spatial distribution of agricultural crops, enhancing agricultural management efficiency, and ensuring food security. With the continuous advancement of remote sensing technologies, achieving efficient and accurate crop classification using remote sensing imagery has become a prominent research focus. Conventional approaches largely rely on empirical rules or single-feature selection (e.g., NDVI or VV) for temporal feature extraction, lacking systematic optimization of multimodal feature combinations from optical and radar data. To address this limitation, this study proposes a crop classification method based on feature-level fusion of multimodal remote sensing data, integrating the complementary advantages of optical and SAR imagery to overcome the temporal and spatial representation constraints of single-sensor observations. The study was conducted in Story County, Iowa, USA, focusing on the growth cycles of corn and soybean. Eight vegetation indices (including NDVI and NDRE) and five polarimetric features (VV and VH) were constructed and analyzed. Using a random forest algorithm to assess feature importance, NDVI+NDRE and VV+VH were identified as the optimal feature combinations. Subsequently, 16 scenes of optical imagery (Sentinel-2) and 30 scenes of radar imagery (Sentinel-1) were fused at the feature level to generate a multimodal temporal feature image with 46 channels. Using Cropland Data Layer (CDL) samples as reference data, a U-Net deep neural network was employed for refined crop classification and compared with single-modal results. Experimental results demonstrated that the fusion model outperforms single-modal approaches in classification accuracy, boundary delineation, and consistency, achieving training, validation, and test accuracies of 95.83%, 91.99%, and 90.81% respectively. Furthermore, consistent improvements were observed across evaluation metrics, including F1-score, precision, and recall. Full article

Early-season sugarcane identification plays a pivotal role in precision agriculture, enabling timely yield forecasting and informed policy-making. Compared to post-season crop identification, early-season identification faces unique challenges, including incomplete temporal observations and spectral ambiguity among crop types in early seasons. Previous studies have not systematically investigated the capability of optical and synthetic aperture radar (SAR) data for early-season sugarcane identification, which may result in suboptimal accuracy and delayed identification timelines. Both the timing for reliable identification (≥90% accuracy) and the earliest achievable timepoint matching post-season level remain undetermined, and which features are effective in the early-season identification is still unknown. To address these questions, this study integrated Sentinel-1 and Sentinel-2 data, extracted 10 spectral indices and 8 SAR features, and employed a random forest classifier for early-season sugarcane identification by means of progressive temporal analysis. It was found that LSWI (Land Surface Water Index) performed best among 18 individual features. Through the feature set accumulation, the seven-dimensional feature set (LSWI, IRECI (Inverted Red-Edge Chlorophyll Index), EVI (Enhanced Vegetation Index), PSSRa (Pigment Specific Simple Ratio a), NDVI (Normalized Difference Vegetation Index), VH backscatter coefficient, and REIP (Red-Edge Inflection Point Index)) achieved the earliest attainment of 90% accuracy by 30 June (early-elongation stage), with peak accuracy (92.80% F1-score) comparable to post-season accuracy reached by 19 August (mid-elongation stage). The early-season sugarcane maps demonstrated high agreement with post-season maps. The 30 June map achieved 88.01% field-level and 90.22% area-level consistency, while the 19 August map reached 91.58% and 93.11%, respectively. The results demonstrate that sugarcane can be reliably identified with accuracy comparable to post-season mapping as early as six months prior to harvest through the integration of optical and SAR data. This study develops a robust approach for early-season sugarcane identification, which could fundamentally enhance precision agriculture operations through timely crop status assessment. Full article

Sandy beaches are vital geomorphic units with ecological, social, and economic significance, playing a key role in coastal protection and ecosystem regulation. However, they are increasingly threatened by climate change and human activities, highlighting the need for large-scale, high-precision monitoring to support sustainable management. Existing remote-sensing-based sandy beach extraction methods face challenges such as suboptimal feature selection and reliance on single data sources, limiting their generalization and accuracy. This study proposes a novel sandy beach extraction framework that integrates multi-source data, feature optimization, and collaborative modeling, with Fujian Province, China, as the study area. The framework combines Sentinel-1/2 imagery, nighttime light data, and terrain data to construct a comprehensive feature set containing 44 spectrum, index, polarization, texture, and terrain variables. The optimal feature subsets are selected using the Recursive Feature Elimination (RFE) algorithm. Six machine learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Gradient Boosting Machine (GBM), Adaptive Boosting (AdaBoost), and Categorical Boosting (CatBoost)—along with an ensemble learning model, are employed for comparative analysis and performance optimization. The results indicate the following. (1) All models achieved the best performance when integrating all five types of features, with the average overall F1-score and accuracy reaching 0.9714 and 0.9733, respectively. (2) The number of optimal features selected by RFE varied by model, ranging from 19 to 36. The ten most important features across models were Band 2 (B2), Elevation, Band 3 (B3), VVVH_SUM, Spatial Average (SAVG), VH, Enhanced Water Index (EWI), Slope, Variance (VAR), and Normalized Difference Vegetation Index (NDVI). (3) The ensemble learning model outperformed all others, achieving an average overall accuracy, precision, recall, and F1-score of 0.9750, 0.9733, 0.9725, and 0.9734, respectively, under the optimal feature subset. A total of 555 sandy beaches were extracted in Fujian Province, covering an area of 43.60 km² with a total perimeter of 1263.59 km. This framework demonstrates strong adaptability and robustness in complex coastal environments, providing a scalable solution for intelligent sandy beach monitoring and refined resource management. Full article

Background: Immunocompromised and clinically fragile individuals are at increased risk of herpes zoster (HZ), but vaccine uptake remains low due to organizational barriers and vaccine hesitancy (VH). This study aimed to evaluate the impact of a multidisciplinary hospital-based counseling model on attitudes toward the recombinant adjuvanted zoster vaccine (RZV). The primary objective was to assess changes in VH over time using the Vaccination Attitudes Examination (VAX) scale; secondary objectives included identifying factors associated with VH, evaluating vaccine safety, and monitoring post-vaccination HZ incidence. Methods: A prospective cohort study was conducted in a large research hospital in Rome, in collaboration with the Local Health Authority. Eligible patients were offered individualized counseling and administered two doses of the RZV. VH was assessed using the 12-item VAX scale at baseline and at one-year follow-up. Multivariable linear regression analysis was performed to investigate associations between baseline characteristics and VH scores. Results: Between July 2022 and July 2023, 178 patients were enrolled, of whom 90 completed the one-year follow-up. Baseline VH was moderate (mean VAX: 2.291/6.000); higher scores were significantly associated with younger age, female sex, and rheumatologic disease (p < 0.05). After the intervention, VAX scores improved significantly across all subscales, particularly in trust in vaccine safety and benefits (p < 0.001). RZV was well tolerated; adverse events were mild and transient. Breakthrough HZ occurred in 3.33% of cases during follow-up. Conclusions: A multidisciplinary hospital-based model effectively improved vaccine attitudes and acceptance in fragile patients. Tracking VH over time with validated tools offers insights for scaling targeted interventions in high-risk groups. Full article

COVID-19 vaccine hesitancy (VH), like attitudes towards other vaccines, is a critical global public health concern. Despite numerous studies covering psychological, sociodemographic, and other determinants of vaccine acceptance, resistance, and hesitance, few studies have reported these factors among students, particularly in politically unstable settings like Ukraine. This cross-sectional, descriptive, and quantitative study assesses hesitancy towards COVID-19 vaccines, utilizing the 5Cs Model. Among 936 respondents surveyed in 2023, 64% received at least one shot of the COVID-19 vaccine (acceptant), 11% were still considering getting vaccinated (hesitant), and 25% refused vaccination (resistant). Vaccination behavior is significantly associated with the 5Cs. Higher collective responsibility significantly increased acceptance and reduced resistance, while higher constraints lowered the chances of being either acceptant or resistant. Confidence protected against resistance. Complacency, counterintuitively, reduced odds of resistance, pointing to differences between passive hesitancy and active refusal. Male gender and sources of information and misinformation influenced confidence. Collective responsibility was positively associated with official sources and negatively with conspiracy beliefs. Complacency increased with official sources, while constraints and calculation were least explained by predictors. Practical barriers should be tackled through improved accessibility and fostering collective responsibility via targeted communication strategies. These findings provide actionable insights for policymakers, healthcare providers, and academic institutions to enhance vaccine uptake among university students, particularly in crisis settings. Full article

Background/Objectives: Gut microbiota research has gained momentum in recent years broadening knowledge of microbial components and their potential effects on health and well-being. Strong association between explicit microbes and metabolic diseases associated with obesity and type 2 diabetes mellitus, gastrointestinal disorders, neurodegenerative diseases, and even cancers have been established. Akkermansia muciniphila is a budding next-generation probiotic that plays an important role in systemic metabolism, intestinal health, and immune regulation, establishing strong implications for its use as a potent therapeutic intervention in diverse diseases. This project aimed at evaluating whether bacterial cell extracts of VH Akkermansia muciniphila (Vidya Strain; VS) can stimulate insulin secretion in INS-1 pancreatic beta cells and GLP-1 secretion in NCI-H716 human L-cells, both established in vitro models for studying metabolic regulation. Methods: Cultured VH Akkermansia muciniphila extracts were administered in a dose-dependent manner on INS-1 cells, and glucose-stimulated insulin secretion (GSIS) was measured via ELISA. Treated Human L-cell lines (NCI-H716) were analyzed for GLP-1 secretion. Results: Our study demonstrated that VH Akkermansia muciniphila extracts modestly increase insulin secretion from INS-1 beta cells and, more notably, induce a robust, dose-dependent rise in GLP-1 secretion from NCI-H716 L-cells, with the highest dose achieving over a 2000% increase comparable to glutamine. Conclusions: These findings suggest that VH A. muciniphila extracts may offer metabolic benefits by enhancing GLP-1 release, highlighting their potential for managing type 2 diabetes and obesity. Full article

Background: Vaccine hesitancy (VH) represents a growing concern among healthcare professionals and students, potentially undermining public health efforts. Nursing, pediatric nursing, and midwifery students are future vaccinators and educators, making it essential to understand their attitudes, knowledge, and confidence toward vaccination. This study aims to assess vaccine-related perceptions and behaviors among these student populations in an Italian university. Methods: A cross-sectional survey was conducted between November 2022 and February 2024 at the University of Rome “Tor Vergata”. A structured, anonymous questionnaire, including the Vaccination Attitudes Examination (VAX) scale, vaccine knowledge items, and sources of information, was administered to students in nursing (n = 205), pediatric nursing (n = 46), and midwifery (n = 21). Statistical analyses included descriptive statistics, ANOVA, post hoc tests, and Mann–Whitney U tests. Results: Among the 272 participants, 20.6% reported refusing at least one recommended vaccine, and 18.4% delayed vaccination for non-medical reasons. Vaccine knowledge and confidence increased significantly with academic progression (p < 0.001). Midwifery students showed both the highest concern for long-term vaccine effects and the greatest confidence in vaccine safety. Institutional and scientific sources were the most trusted, though traditional and non-institutional media also influenced perceptions, particularly among midwifery students. Conclusions: Despite high COVID-19 vaccine uptake, VH persists among health professional students. Discipline-specific patterns highlight the need for early, targeted educational strategies to enhance vaccine literacy and reduce hesitancy. Tailored training may empower future professionals to become informed and credible advocates for vaccination. Full article