Search Results (82)

Background/Objectives: Melanoma is an aggressive type of skin cancer that poses serious health risks if not detected in its early stages. Although early diagnosis enables effective treatment, delays can result in life-threatening consequences. Traditional diagnostic processes predominantly rely on the subjective expertise of dermatologists, which can lead to variability and time inefficiencies. Consequently, there is an increasing demand for automated systems that can accurately classify melanoma lesions and retrieve visually similar cases to support clinical decision-making. Methods: This study proposes a transfer learning (TL)-based deep learning (DL) framework for the classification of melanoma images and the enhancement of content-based image retrieval (CBIR) systems. Pre-trained models including DenseNet121, InceptionV3, Vision Transformer (ViT), and Xception were employed to extract deep feature representations. These features were integrated using a weighted fusion strategy and classified through an Ensemble learning approach designed to capitalize on the complementary strengths of the individual models. The performance of the proposed system was evaluated using classification accuracy and mean Average Precision (mAP) metrics. Results: Experimental evaluations demonstrated that the proposed Ensemble model significantly outperformed each standalone model in both classification and retrieval tasks. The Ensemble approach achieved a classification accuracy of 95.25%. In the CBIR task, the system attained a mean Average Precision (mAP) score of 0.9538, indicating high retrieval effectiveness. The performance gains were attributed to the synergistic integration of features from diverse model architectures through the ensemble and fusion strategies. Conclusions: The findings underscore the effectiveness of TL-based DL models in automating melanoma image classification and enhancing CBIR systems. The integration of deep features from multiple pre-trained models using an Ensemble approach not only improved accuracy but also demonstrated robustness in feature generalization. This approach holds promise for integration into clinical workflows, offering improved diagnostic accuracy and efficiency in the early detection of melanoma. Full article

The Arctic Ocean is turning blue. Abrupt Arctic warming and amplification is driving rapid sea ice decline and irreversible deglaciation of Greenland. The already emerging, substantial consequences for the planet and society are intensifying and yet, model-based projections lack validatory consensus. To date, we cannot anticipate how a blue Arctic will respond to and amplify an increasingly warmer future climate, nor how it will impact the wider planet and society. Climate projections are inconclusive as we critically lack key Arctic geological archives that preserved the answers. This “Arctic Challenge” of global significance can only be addressed by investigating the processes, consequences, and impacts of past “greenhouse” (warmer-than-present) climate states. To address this challenge, the ERC Synergy Grant project Into the Blue (i2B) is undertaking a program of research focused on retrieving new Arctic geological archives of past warmth and key breakthroughs in climate model performance to deliver a ground-breaking, synergistic framework to answer the central question: “Why and what were the global ramifications of a “blue” (ice-free) Arctic during past warmer-than-present climates?” Here, we present the proposed research plan that will be conducted as part of this program. Into the Blue will quantify cryosphere (sea ice and land ice) change in a warmer world that will form the scientific basis for understanding the dynamics of Arctic cryosphere and ocean changes to enable the quantitative assessment of the impact of Arctic change on ocean biosphere, climate extremes, and society that will underpin future cryosphere-inclusive IPCC assessments. Full article

Magnetic nanoparticles (MNPs), especially iron oxide (Fe₃O₄), display distinctive superparamagnetic characteristics and elevated surface-area-to-volume ratios, facilitating improved physicochemical interactions with solutes and pollutants. These characteristics make MNPs strong contenders for use in water treatment applications. This research investigates the application of iron oxide MNPs synthesized via co-precipitation as innovative draw solutes in forward osmosis (FO) for treating synthetic produced water (SPW). The FO membrane underwent surface modification with sulfobetaine methacrylate (SBMA), a zwitterionic polymer, to increase hydrophilicity, minimize fouling, and elevate water flux. The SBMA functional groups aid in electrostatic repulsion of organic and inorganic contaminants, simultaneously encouraging robust hydration layers that improve water permeability. This adjustment is vital for sustaining consistent flux performance while functioning with MNP-based draw solutions. Material analysis through thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and Fourier-transform infrared spectroscopy (FTIR) verified the MNPs’ thermal stability, consistent morphology, and modified surface chemistry. The FO experiments showed a distinct relationship between MNP concentration and osmotic efficiency. At an MNP dosage of 10 g/L, the peak real-time flux was observed at around 3.5–4.0 L/m²·h. After magnetic regeneration, 7.8 g of retrieved MNPs generated a steady flow of ~2.8 L/m²·h, whereas a subsequent regeneration (4.06 g) resulted in ~1.5 L/m²·h, demonstrating partial preservation of osmotic driving capability. Post-FO draw solutions, after filtration, exhibited total dissolved solids (TDS) measurements that varied from 2.5 mg/L (0 g/L MNP) to 227.1 mg/L (10 g/L MNP), further validating the effective dispersion and solute contribution of MNPs. The TDS of regenerated MNP solutions stayed similar to that of their fresh versions, indicating minimal loss of solute activity during the recycling process. The combined synergistic application of SBMA-modified FO membranes and regenerable MNP draw solutes showcases an effective and sustainable method for treating produced water, providing excellent water recovery, consistent operational stability, and opportunities for cyclic reuse. Full article

Under the dual pressures of global climate warming and rapid urbanization, the Yangtze River Basin, as the world’s largest urban agglomeration, is facing intensifying thermal environmental stress. Although river ecosystems demonstrate significant thermal regulation functions, their spatial thresholds of cooling effects and multiscale driving mechanisms have remained to be systematically elucidated. This study retrieved land surface temperature (LST) using the split window algorithm and quantitatively analyzed the changes in the river cold island effect and its driving mechanisms in the Yangtze River Basin by combining multi-ring buffer analysis and the optimal parameter-based geographical detector model. The results showed that (1) forest land is the main land use type in the Yangtze River Basin, with built-up land having the largest area increase. Affected by natural, socioeconomic, and meteorological factors, the summer temperatures displayed a spatial pattern of “higher in the east than the west, warmer in the south than the north”. (2) There are significant differences in the cooling magnitude among different land types. Forest land has the maximum daytime cooling distance (589 m), while construction land has the strongest cooling magnitude (1.72 °C). The cooling effect magnitude is most pronounced in upstream areas of the basin, reaching 0.96 °C. At the urban agglomeration scale, the Chengdu–Chongqing urban agglomeration shows the greatest temperature reduction of 0.90 °C. (3) Elevation consistently demonstrates the highest explanatory power for LST spatial variability. Interaction analysis shows that the interaction between socioeconomic factors and elevation is generally the strongest. This study provides important spatial decision support for formulating basin-scale ecological thermal regulation strategies based on refined spatial layout optimization, hierarchical management and control, and a “natural–societal” dual-dimensional synergistic regulation system. Full article

Soil acidification, salinization, and heavy metal pollution pose serious threats to global food security and sustainable agricultural development. Biochar, with its high porosity, large surface area, and abundant functional groups, can effectively improve soil properties. However, due to variations in feedstocks and pyrolysis conditions, it may contain potentially harmful substances. Industrial wastes such as fly ash, steel slag, red mud, and phosphogypsum are rich in minerals and show potential for soil improvement, but direct application may pose environmental risks. The co-application of biochar with these wastes can produce composite amendments that enhance pH buffering capacity, nutrient availability, and pollutant immobilization. Therefore, a review of biochar-industrial waste composites as soil amendments is crucial for addressing soil degradation and promoting resource utilization of wastes. In this study, the literature was retrieved from Web of Science, Scopus, and Google Scholar using keywords including biochar, fly ash, steel slag, red mud, phosphogypsum, combined application, and soil amendment. A total of 144 articles from 2000 to 2025 were analyzed. This review summarizes the physicochemical properties of biochar and representative industrial wastes, including pH, electrical conductivity, surface area, and elemental composition. It examines their synergistic mechanisms in reducing heavy metal release through adsorption, complexation, and ion exchange. Furthermore, it evaluates the effects of these composites on soil health and crop productivity, showing improvements in soil structure, nutrient balance, enzyme activity, and metal immobilization. Finally, it identifies knowledge gaps as well as future prospects and recommends long-term field trials and digital agriculture technologies to support the sustainable application of these composites in soil management. Full article

Due to its persistence and potential negative effects on ecosystems and human health, microplastic pollution in aquatic environments has become a major worldwide concern. Photocatalytic degradation is a sustainable manner to degrade microplastics to non-toxic by-products. In this review, comprehensive discussion focuses on the synergistic effects of various photocatalytic materials including TiO₂, ZnO, WO₃, graphene oxide, and metal–organic frameworks for producing heterojunctions and involving multidimensional nanostructures. Such mechanisms can include the generation of reactive oxygen species and polymer chain scission, which can lead to microplastic breakdown and mineralization. The advancements of material modifications in the (nano)structure of photocatalysts, doping, and heterojunction formation methods to promote UV and visible light-driven photocatalytic activity is discussed in this paper. Reactor designs, operational parameters, and scalability for practical applications are also reviewed. Photocatalytic systems have shown a lot of development but are hampered by shortcomings which include a lack of complete mineralization and production of intermediary secondary products; variability in performance due to the fluctuation in the intensity of solar light, limited UV light, and environmental conditions such as weather and the diurnal cycle. Future research involving multifunctional, environmentally benign photocatalytic techniques—e.g., doped composites or composite-based catalysts that involve adsorption, photocatalysis, and magnetic retrieval—are proposed to focus on the mechanism of utilizing light effectively and the environmental safety, which are necessary for successful operational and industrial-scale remediation. Full article

This review presents an in-depth analysis of the synergistic role of molecular biology in advancing forensic entomology. The study discusses how insects associated with decomposing bodies provide critical data for estimating the post-mortem interval (PMI), and how molecular techniques improve species identification and trace analysis. The manuscript examines DNA-based methods such as RAPD, RFLP, and mitochondrial sequencing, along with innovative applications like gene expression profiling and entomotoxicology analysis. Additionally, it presents real case studies illustrating how molecular data from insects can be used not only to estimate PMI but also to identify victims or suspects through human DNA retrieved from insect tissues. These advances confirm the fundamental role of molecular biology in strengthening the reliability and applicability of forensic entomology in legal contexts. Full article

Urban blue–green space (UBGS) plays a critical role in mitigating the urban heat island (UHI) effect and reducing land surface temperatures (LSTs). However, existing research has not sufficiently explored the optimization of UBGS spatial configurations or their interactions with urban morphology. This study takes New York City as a case and systematically investigates small-scale urban cooling strategies by integrating multiple factors, including adjustments to the blue–green ratio, spatial layouts, vegetation composition, building density, building height, and layout typologies. We utilize multi-source geographic data, including LiDAR derived land cover, OpenStreetMap data, and building footprint data, together with LST data retrieved from Landsat imagery, to develop a prediction model based on generative adversarial networks (GANs). This model can rapidly generate visual LST predictions under various configuration scenarios. This study employs a combination of qualitative and quantitative metrics to evaluate the performance of different model stages, selecting the most accurate model as the final experimental framework. Furthermore, the experimental design strictly controls the study area and pixel allocation, combining manual and automated methods to ensure the comparability of different ratio configurations. The main findings indicate that a blue–green ratio of 3:7 maximizes cooling efficiency; a shrub-to-tree coverage ratio of 2:8 performs best, with tree-dominated configurations outperforming shrub-dominated ones; concentrated linear layouts achieve up to a 10.01% cooling effect; and taller buildings exhibit significantly stronger UBGS cooling performance, with super-tall areas achieving cooling effects approximately 31 percentage points higher than low-rise areas. Courtyard layouts enhance airflow and synergistic cooling effects, whereas compact designs limit the cooling potential of UBGS. This study proposes an innovative application of GANs to address a key research gap in the quantitative optimization of UBGS configurations and provides a methodological reference for sustainable microclimate planning at the neighborhood scale. Full article

Objective: This study aims to elucidate the potential mechanisms by which Fespixon cream promotes diabetic foot ulcer (DFU) healing using network pharmacology, molecular docking, and RT-qPCR validation in clinical tissue samples. Methods: Active components of Fespixon cream were screened from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and relevant literature, and their corresponding targets were standardized using the Universal Protein Resource (UniProt) database. Diabetic foot ulcer (DFU)-related targets were retrieved and filtered from the GeneCards database and the Online Mendelian Inheritance in Man (OMIM) database. The intersection of drug and disease targets was identified, and a protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The interaction network was visualized using Cytoscape version 3.7.2 software. The potential mechanisms of the shared targets were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using R software packages, and results were visualized through Bioinformatics online tools. Molecular docking was performed to validate the binding between key active compounds of Fespixon cream and core DFU targets using AutoDock Vina version 1.1.2 and PyMOL software. Furthermore, RT-qPCR analysis was performed on wound edge tissue samples from DFU patients treated with Fespixon cream to experimentally verify the mRNA expression levels of predicted hub genes. Results: Network pharmacology analysis identified eight active compounds in Fespixon cream, along with 153 potential therapeutic targets related to diabetic foot ulcer (DFU). Among these, 21 were determined as core targets, with the top five ranked by degree value being RAC-αserine/threonine-protein kinase (AKT1), Cellular tumor antigen p53 (TP53), Tumor necrosis factor (TNF), Interleukin-6 (IL6), and Mitogen-activated protein kinase 1 (MAPK1). GO enrichment analysis indicated that the targets of Fespixon cream were primarily involved in various biological processes related to cellular stress responses. KEGG pathway enrichment revealed that these targets were significantly enriched in pathways associated with diabetic complications, atherosclerosis, inflammation, and cancer. Molecular docking confirmed stable binding interactions between the five major active compounds—quercetin, apigenin, rosmarinic acid, salvigenin, and cirsimaritin—and the five core targets (AKT1, TP53, TNF, IL6, MAPK1). Among them, quercetin exhibited the strongest binding affinity with AKT1. RT-qPCR validation in clinical DFU tissue samples demonstrated consistent expression trends with computational predictions: AKT1 was significantly upregulated, while TP53, TNF, IL6, and MAPK1 were markedly downregulated in the Fespixon-treated group compared to controls (p < 0.001), supporting the proposed multi-target therapeutic mechanism. Conclusions: Our study reveals the potential mechanisms by which Fespixon cream exerts therapeutic effects on DFUs. The efficacy of Fespixon cream in treating DFUs is attributed to the synergistic actions of its bioactive components through multiple targets and multiple signaling pathways. Full article

Canopy chlorophyll content (CCC) is a key indicator for assessing the carbon sequestration capacity and material cycling efficiency of ecosystems, and its accurate retrieval holds significant importance for analyzing ecosystem functioning. Although numerous destructive and remote sensing methods have been developed to estimate CCC, the accurate estimation of CCC remains a significant challenge in mountainous regions with complex terrain and heterogeneous vegetation types. Through the synergistic analysis of ground hyperspectral and Sentinel-2 data, this study employed Pearson correlation analysis and spectral resampling techniques to identify Sentinel-2 blue band B1 (443 nm) and red band B4 (665 nm) as chlorophyll-sensitive bands through spectral matching with the hyperspectral reflectance of typical grassland vegetation. Based on this, we developed a new four-band vegetation index (VI), the Dual Red-edge and Coastal Aerosol Vegetation Index (DRECAVI), for estimating the CCC of heterogeneous grasslands in the middle section of the Tianshan Mountains. DRECAVI incorporates red-edge anti-saturation modules (bands B4 and B7) and aerosol correction modules (bands B1 and B8). In order to test the performance of the new index, we compared it with eight commonly used indices and a hybrid model, the Sentinel-2 Biophysical Processor (S2BP). The results indicated the following: (1) DRECAVI demonstrated the highest accuracy in CCC retrieval for mountainous vegetation (R² = 0.74, RMSE = 16.79, MAE = 12.50) compared to other VIs and hybrid methods, effectively mitigating saturation effects in high biomass areas and capturing a weak bimodal distribution pattern of CCC in the montane meadow. (2) The blue band B1 enhances atmospheric correction robustness by suppressing aerosol scattering, and the red-edge band B7 overcomes the sensitivity limitations of conventional red-edge indices (such as NDVI705, CIred-edge, and NDRE), demonstrating the potential application of the synergy mechanism between the blue band and the red-edge band. (3) Although the S2BP achieved high accuracy (R² = 0.73, RMSE = 19.83, MAE = 14.71) without saturation effects and detected a bimodal distribution of CCC in the montane meadow of the study area, its algorithmic complexity hindered large-scale operational applications. In contrast, DRECAVI maintained similar precision while reducing algorithmic complexity, making it more suitable for regional-scale grassland dynamic monitoring. This study confirms that the synergistic use of multi-source data effectively overcomes the limitations of the spectral–spatial resolution of a single data source, providing a novel methodology for the precision monitoring of mountain ecosystems. Full article

A reliable ocean surface current (OSC) estimate is difficult to retrieve from synthetic aperture radar (SAR) data due to the challenge of accurately partitioning the Doppler shifts induced by wind waves and OSC. Recent research on SAR-based OSC retrieval is typically based on the assumption that the SAR Doppler shifts caused by wind waves and OSC are linearly superimposed. However, this assumption may lead to large errors in regions where nonlinear wave–current interactions are significant. To address this issue, we developed a novel deep learning model, OSCNet, for OSC retrieval. The model leverages Sentinel-1 Interferometric Wide (IW) Level 2 Ocean products collected from July 2023 to September 2024, combined with wave data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and geostrophic currents from newly available SWOT Level 3 products. The OSCNet model is optimized by refining input ocean surface physical parameters and introducing a ResNet structure. Moreover, the Normalized Radar Cross-Section (NRCS) is incorporated to account for wave breaking and backscatter effects on Doppler shift estimates. The retrieval performance of the OSCNet model is evaluated using SWOT data. The mean absolute error (MAE) and root mean square error (RMSE) are found to be 0.15 m/s and 0.19 m/s, respectively. This result demonstrates that the OSCNet model enhances the retrieval of OSC from SAR data. Furthermore, a mesoscale eddy detected in the OSC map retrieved by OSCNet is consistent with the collocated sea surface chlorophyll-a observation, demonstrating the capability of the proposed method in capturing the variability of mesoscale eddies. Full article

Aerosols play a crucial role in air quality, climate regulation, and public health; their timely monitoring is hence fundamental. The aerosol optical depth (AOD) is the parameter used to investigate the spatial–temporal distribution of aerosols from space. Specifically, the AOD retrieved from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm applied to a Moderate Resolution Imaging Spectroradiometer (MODIS) is suitable for aerosol investigation at a local scale by exploiting its high spatial resolution (1 km × 1 km). In this study, the MAIAC AOD retrieval over Rome (Italy) was validated for the first time, using ground-based data provided by an AERONET station operating in a semi-rural environment close to the city, over a time series from January 2001 to December 2022. Moreover, AOD trends were evaluated in a study area encompassing Rome and its surroundings, characterized by a transition zone between urban and rural environments. The results show a general underestimation of the MAIAC AOD; specifically, the validation process highlighted the less accurate performance of the algorithm under higher aerosol loading and with predominantly coarse mode aerosol. Interesting results were obtained concerning the influence of the geometrical configuration of satellite acquisition on the accuracy of the MAIAC product. In particular, the solar zenith angle, the relative azimuth and the scattering angle between the principal plane of the sun and satellite synergistically influence retrievals. Finally, the spatial distribution of the AOD shows a decreasing trend over the 2001–2022 period and a strong influence of the city of Rome over the whole study area. Full article

The widespread presence of antibiotics in aquatic environments poses significant ecological and public health risks due to their persistence, antimicrobial activity, and contribution to resistance gene proliferation. This review systematically evaluated the advancements in antibiotic degradation using ionizing radiation (γ-rays and electron beam) from laboratory studies to practical applications. By using keywords such as “antibiotic degradation” and “ionizing irradiation OR gamma radiation OR electron beam,” 328 publications were retrieved from Web of Science, with China contributing 33% of the literature, and a number of global representative studies were selected for in-depth discussion. The analysis encompassed mechanistic insights into oxidative (•OH) and reductive (e_aq⁻) pathways, degradation kinetics influenced by absorbed dose (1–10 kGy), initial antibiotic concentration, pH, and matrix complexity. The results demonstrated ≥90% degradation efficiency for major antibiotic classes (macrolides, β-lactams, quinolones, tetracyclines, and sulfonamides), though mineralization remains suboptimal (<50% TOC removal). Synergistic integration with peroxymonosulfate (PMS), H₂O₂, or O₃ enhances mineralization rates. This review revealed that ionizing radiation is a chemical-free, compatible, and highly efficient technology with effective antibiotic degradation potential. However, it still faces several challenges in practical applications, including incomplete mineralization, matrix complexity in real wastewater, and operating costs. Further improvements and optimization, such as hybrid system development (e.g., coupling electron beam with other conventional technologies, such as flocculation, membrane separation, anaerobic digestion, etc.), catalytic enhancement, and life-cycle assessments of this emerging technology would be helpful for promoting its practical environmental application. Full article

Industrial defect detection in edge computing environments faces critical challenges in balancing accuracy, efficiency, and adaptability under data scarcity. To address these limitations, we propose the Hybrid Anomaly Detection System (HyADS), a novel lightweight framework for edge-based industrial defect detection. HyADS integrates three synergistic modules: (1) a feature extractor that integrates Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) to capture robust texture features, (2) a lightweight U-net autoencoder that reconstructs normal patterns while preserving spatial details to highlight small-scale defects, and (3) an adaptive patch matching module inspired by memory bank retrieval principles to accurately localize local outliers. These components are synergistically fused and then fed into a segmentation head that unifies global reconstruction errors and local anomaly maps into pixel-accurate defect masks. Extensive experiments on the MVTec AD, NEU, and Severstal datasets demonstrate state-of-the-art performance. Notably, HyADS achieves state-of-the-art F1 scores (94.1% on MVTec) in anomaly detection and IoU scores (85.5% on NEU/82.8% on Seversta) in segmentation. Designed for edge deployment, this framework achieves real-time inference (40–45 FPS on an RTX 4080 GPU) with minimal computational overheads, providing a practical solution for industrial quality control in resource-constrained environments. Full article

While deep learning-based image matching methods excel at extracting high-level semantic features from remote sensing data, their performance degrades significantly under cross-daylight conditions and wide-baseline geometric distortions, particularly in multi-source street-view scenarios. This paper presents a novel illumination-invariant framework that synergistically integrates geometric topology and semantic consistency to achieve robust multi-view matching for cross-daylight urban perception. We first design a self-supervised learning paradigm to extract illumination-agnostic features by jointly optimizing local descriptors and global geometric structures across multi-view images. To address extreme perspective variations, a homography-aware transformation module is introduced to stabilize feature representation under large viewpoint changes. Leveraging a graph neural network with hierarchical attention mechanisms, our method dynamically aggregates contextual information from both local keypoints and semantic topology graphs, enabling precise matching in occluded regions and repetitive-textured urban scenes. A dual-branch learning strategy further refines similarity metrics through supervised patch alignment and unsupervised spatial consistency constraints derived from Delaunay triangulation. Finally, a topology-guided multi-plane expansion mechanism propagates initial matches by exploiting the inherent structural regularity of street scenes, effectively suppressing mismatches while expanding coverage. Extensive experiments demonstrate that our framework outperforms state-of-the-art methods, achieving a 6.4% improvement in matching accuracy and a 30.5% reduction in mismatches under cross-daylight conditions. These advancements establish a new benchmark for reliable multi-source image retrieval and localization in dynamic urban environments, with direct applications in autonomous driving systems and large-scale 3D city reconstruction. Full article