Search Results (374)

Baiyangdian Lake, recognized as the largest freshwater body in northern China, plays a vital role in maintaining the regional eco-environment. Prior studies have pointed out the contamination of sediments with heavy metals, raising concerns about eco-environmental challenges. Therefore, it is imperative to evaluate the current pollution levels and ecological threats related to heavy metals found in the sediments of Baiyangdian Lake as well as in its inflow rivers. In May 2022, surface sediments with a depth of less than 20 cm were analyzed for Cu, Zn, Pb, Cr, Ni, As, Cd, and Hg to determine the pollution status, identify sources of pollution, and evaluate potential ecological risks. A range of evaluation methods used by predecessors such as geo-accumulation index (I_geo), enrichment factor (EF), ecological risk index (RI), sediment quality guidelines (SQGs), positive matrix factorization (PMF), absolute principal component score-multiple linear regression model (APCS-MLR), chemical mass balance (CMB), and UNMIX model were analyzed. After comparison, multi-methods including the geo-accumulation index (Igeo), absolute principal component score-multiple linear regression model (APCS-MLR), ecological risk index (RI), and sediment quality guidelines (SQGs) were utilized this time, leading to a better result. Findings reveal that pollution levels are generally low or non-existent, with only 1.64% of sampling sites showing close to moderate pollution levels for Cu, Pb, and Zn, and 4.92% and 1.64% of sites exhibiting close to moderate and moderate pollution levels for Cd, respectively. The main contributors to heavy metal presence are pinpointed as industrial wastewater discharge, particularly Cu, Zn, Pb, Cd, and Hg. The ecological risks are also relatively low, with 4.92%, 1.64%, and 1.64% of sampling sites demonstrating close to moderate, moderate, and strong risks in the inflow rivers, respectively. Additionally, only one site shows moderate potential biological toxicity, while the rest display non-toxicity. These findings will update our cognition and offer a scientific basis for pollution treatment and ecosystem enhancement for government management. Full article

The timely updating of the spatial distribution of buildings is essential to understanding a city’s development. Deep learning methods have remarkable benefits in quickly and accurately recognizing these changes. Current semi-supervised change detection (SSCD) methods have effectively reduced the reliance on labeled data. However, these methods primarily focus on utilizing unlabeled data through various training strategies, neglecting the impact of pseudo-changes and learning bias in models. When dealing with limited labeled data, abundant low-quality pseudo-labels generated by poorly performing models can hinder effective performance improvement, leading to the incomplete recognition results of changes to buildings. To address this issue, we propose a feature multi-scale information interaction and complementation semi-supervised method based on consistency regularization (MSFG-SemiCD), which includes a multi-scale feature fusion-guided change detection network (MSFGNet) and a semi-supervised update method. Among them, the network facilitates the generation of multi-scale change features, integrates features, and captures multi-scale change targets through the temporal difference guidance module, the full-scale feature fusion module, and the depth feature guidance fusion module. Moreover, this enables the fusion and complementation of information between features, resulting in more complete change features. The semi-supervised update method employs a weak-to-strong consistency framework to achieve model parameter updates while maintaining perturbation invariance of unlabeled data at both input and encoder output features. Experimental results on the WHU-CD and LEVIR-CD datasets confirm the efficacy of the proposed method. There is a notable improvement in performance at both the 1% and 5% levels. The IOU in the WHU-CD dataset increased by 5.72% and 6.84%, respectively, while in the LEVIR-CD dataset, it improved by 18.44% and 5.52%, respectively. Full article

A machine learning algorithm combined with measurements obtained by a NILU-UV irradiance meter enables the determination of total ozone column (TOC) amount and cloud optical depth (COD). In the New York City area, a NILU-UV instrument on the rooftop of a Stevens Institute of Technology building (40.74° N, −74.03° E) has been used to collect data for several years. Inspired by a previous study [Opt. Express 22, 19595 (2014)], this research presents an updated neural-network-based method for TOC and COD retrievals. This method provides reliable results under heavy cloud conditions, and a convenient algorithm for the simultaneous retrieval of TOC and COD values. The TOC values are presented for 2014–2023, and both were compared with results obtained using the look-up table (LUT) method and measurements by the Ozone Monitoring Instrument (OMI), deployed on NASA’s AURA satellite. COD results are also provided. Full article

Improved navigability can enhance inland waterway transportation efficiency, contributing to synchro-modal logistics and promoting sustainable development in regions that can benefit from the presence of considerable waterways. Modern technological solutions, such as digital twins in corridor management systems, must integrate functions of navigability forecasts that provide timely and reliable information for safe trip planning. This information needs to account for the type of vessel and for the environmental and geomorphological characteristics of each navigation trait. This paper presents a case study, within the EU project CRISTAL, focusing on the Italian Po River, of which the navigability forecast requirements of a digital twin are illustrated. Preliminary results to deliver navigability risk information were obtained. In particular, the statistical correlation of water discharge and water depth, computed from historical data, suggested that efficient forecast models for navigability risk, given some water discharge forecasts, could be built. To this aim, the LSTM (long-short-term-memory) technique was used on the same data to provide models linking water discharge and water depth predictions. Future work involves further testing these models with updated real data and integrating outcomes with climatic and infrastructure management information to enhance the accuracy of the risk information. Full article

Iron-based shape memory alloys (Fe-SMAs), traditionally manufactured, are favored in engineering applications owing to their cost-effectiveness and ease of fabrication. However, the conventional manufacturing process of Fe-SMAs is time-consuming and raw-material-wasting. In contrast, additive manufacturing (AM) technology offers a streamlined approach to the integral molding of materials, significantly reducing raw material usage and fabrication time. Despite its potential, research on AMed Fe-SMAs remains in its early stages. This review provides updated information on current AM technologies utilized for Fe-SMAs and their applications. It provides an in-depth discussion on how printing parameters, defects, and post-printing microstructure control affect the mechanical properties and shape memory effect (SME) of AMed Fe-SMAs. Furthermore, this review identifies existing challenges in the AMed Fe-SMA approach and proposes future research directions, highlighting potential areas for development. The insights presented aim to guide improvements in the material properties of AMed Fe-SMAs by optimizing printing parameters and enhancing the SME through microstructure adjustment. Full article

Vilca or cebil (Anadenanthera colubrina var. cebil) is a species known for its psychoactive properties and its widespread use among the pre-Hispanic peoples who inhabited the southern Andean area (southern Peru, Bolivia, northern Chile and northwest Argentina). Studies on this species, as well as on medicinal, psychoactive, or magical plants in general, tend to consider its use in post-Spanish conquest times to be scarce or irrelevant in the Andes of South America. However, based on an in-depth review of the existing literature and on ethnobotanical research conducted in markets in Argentina, Bolivia, and Peru, this paper provides an updated overview affirming the continuity of the use of this species. The results indicate a significant diversity in terms of usage types, plant parts used, treatments, and conditions in which it is applied, along with new records of vernacular names. This paper also offers an interpretation from the perspective of Andean logics, highlighting the current therapeutic effectiveness of the seeds of this plant, facilitated through a series of “movements” that aim to restore the affected person’s health. Full article

In mineral, environmental, and engineering explorations, we frequently encounter geological bodies with varied sizes, depths, and conductivity contrasts with surround rocks and try to interpret them with single survey data. The conventional three-dimensional (3-D) inversions significantly rely on the size of the grids, which should be smaller than the smallest geological target to achieve a good recovery to anomalous electric conductivity. However, this will create a large amount of unknowns to be solved and cost significant time and memory. In this paper, we present a multi-scale (MS) stochastic inversion scheme based on shearlet transform for airborne electromagnetic (AEM) data. The shearlet possesses the features of multi-direction and multi-scale, allowing it to effectively characterize the underground conductivity distribution in the transformed domain. To address the practical implementation of the method, we use a compressed sensing method in the forward modeling and sensitivity calculation, and employ a preconditioner that accounts for both the sampling rate and gradient noise to achieve a fast stochastic 3-D inversion. By gradually updating the coefficients from the coarse to fine scales, we obtain the multi-scale information on the underground electric conductivity. The synthetic data inversion shows that the proposed MS method can better recover multiple geological bodies with different sizes and depths with less time consumption. Finally, we conduct 3-D inversions of a field dataset acquired from Byneset, Norway. The results show very good agreement with the geological information. Full article

Background: Burns are a common and severe medical emergency requiring immediate specialized care to minimize damage and prevent complications. Burn severity depends on depth, extent, and location, with more complex care needed for burns on critical areas or extensive burns. Nursing is essential in burn management, providing immediate care, adapting treatments, managing pain, preventing infections, and offering emotional support for recovery. The study aims to analyse the epidemiological and clinical characteristics of burns treated at the Hospital Emergency Department of the Hospital Complex of Cáceres (Spain) from January 2018 to December 2022. It looks at factors like gender, age, hospital stay duration, emergency type (paediatric or adult), main diagnosis, skin thickness, burn degree, affected body areas, percentage of body surface area burned, and treatment types. It also investigates how treatment varies by gender, age, skin thickness, and burn severity. The relevance of this research lies in the fact that periodic epidemiological studies are essential to monitor changes in diseases, evaluate the effectiveness of interventions, detect outbreaks quickly, update knowledge on risk factors, and guide health policy decisions. This ensures an adapted and effective response to the needs of the population. Methods: Retrospective, observational study that analysed burn cases treated at the Hospital Complex of Cáceres (Spain) 2018–2022. Inclusion criteria were based on ICD-10 codes for burns, excluding severe cases not treated in this service. Data were analysed using descriptive statistics, Student’s t-tests, Chi-square tests, and ANOVA. Results: 220 patients surveyed, with a mean age of 47 years and 60.9% male. Most burns (95.5%) affected the external body surface, with a mean hospital stay of 7.86 days. Medical treatment was provided to 75.5% of patients, and 24.5% required surgical intervention. Significant differences in treatment procedures were observed according to age, skin thickness, and burn degree. Older patients had more procedures and longer hospital stays. Excision and transfer procedures were more common in full-thickness and severe burns. Conclusions: The findings align with previous research on burn demographics and treatment approaches. Treatment differences by age and burn severity highlight the need for tailored interventions. The study underscores the importance of comprehensive burn management, including psychological support for improved long-term outcomes. Further research could explore the impact of socio-economic factors on burn incidence and treatment. This study was not registered. Full article

The perception system plays a crucial role by integrating LiDAR and various sensors to perform localization and object detection, which ensures the security of intelligent driving. However, existing research indicates that LiDAR is vulnerable to sensor attacks, which lead to inappropriate driving strategies and need effective attack recognition methods. Previous LiDAR attack recognition methods rely on fixed anomaly thresholds obtained from depth map data distributions in specific scenarios as static anomaly boundaries, which lead to reduced accuracy, increased false alarm rates, and a lack of performance stability. To address these problems, we propose an adaptive LiDAR attack recognition framework capable of adjusting to different driving scenarios. This framework initially models the perception system by integrating the vehicle dynamics model and object tracking algorithms to extract data features, subsequently employing Gaussian Processes for the probabilistic modeling of these features. Finally, the framework employs sparsification computing techniques and a sliding window strategy to continuously update the Gaussian Process model with window data, which achieves incremental learning that generates uncertainty estimates as dynamic anomaly boundaries to recognize attacks. The performance of the proposed framework has been evaluated extensively using the real-world KITTI dataset covering four driving scenarios. Compared to previous methods, our framework achieves a 100% accuracy rate and a 0% false positive rate in the localization system, and an average increase of 3.43% in detection accuracy in the detection system across the four scenarios, which demonstrates superior adaptive capabilities. Full article

The superpixel usually serves as a region-level feature in various image processing tasks, and is known for segmentation accuracy, spatial compactness and running efficiency. However, since these properties are intrinsically incompatible, there is still a compromise within the overall performance of existing superpixel algorithms. In this work, the property constraint in superpixels is relaxed by in-depth understanding of the image content, and a novel two-stage superpixel generation framework is proposed to produce content-aware superpixels. In the global processing stage, a diffusion-based online average clustering framework is introduced to efficiently aggregate image pixels into multiple superpixel candidates according to color and spatial information. During this process, a centroid relocation strategy is established to dynamically guide the region updating. According to the area feature in manifold space, several superpixel centroids are then split or merged to optimize the regional representation of image content. Subsequently, local updating is adopted on pixels in those superpixel regions to further improve the performance. As a result, the dynamic centroid relocating strategy offers online averaging clustering the property of content awareness through coarse-to-fine label updating. Extensive experiments verify that the produced superpixels achieve desirable and comprehensive performance on boundary adherence, visual satisfactory and time consumption. The quantitative results are on par with existing state-of-the-art algorithms in terms with several common property metrics. Full article

This comprehensive study explores the influence of aerosols and clouds on solar radiation in the urban environments of nine of Greece’s largest cities over the decade from 2014 to 2023. Utilizing a combination of Earth Observation data, radiative transfer models, and geographic information systems, the research undertook digital surface modeling and photovoltaic simulations. The study meticulously calculated the optimal rooftop areas for photovoltaic installation in these cities, contributing significantly to their energy adequacy and achieving a balance between daily electricity production and demand. Moreover, the research provides an in-depth analysis of energy and economic losses, while also highlighting the environmental benefits. These include a reduction in pollutant emissions and a decrease in the carbon footprint, aligning with the global shift towards local energy security and the transformation of urban areas into green, smart cities. The innovative methodology of this study, which leverages open access data, sets a strong foundation for future research in this field. It opens up possibilities for similar studies and has the potential to contribute to the creation of an updated, comprehensive solar potential map for continental Greece. This could be instrumental in climate change mitigation and adaptation strategies, thereby promoting sustainable urban development and environmental preservation. Full article

The skin microbiota, with its millions of bacteria, fungi, and viruses, plays a key role in balancing the health of the skin and scalp. Its continuous exposure to potentially harmful stressors can lead to abnormalities such as local dysbiosis, altered barrier function, pathobiont overabundance, and infections often sustained by multidrug-resistant bacteria. These factors contribute to skin impairment, deregulation of immune response, and chronic inflammation, with local and systemic consequences. In this scenario, according to the needs of the bio-circular-green economy model, novel harmless strategies, both for regulating the diverse epidermal infectious and inflammatory processes and for preserving or restoring the host skin eubiosis and barrier selectivity, are requested. Vitis vinifera L. leaves and their derived extracts are rich in plant secondary metabolites, such as polyphenols, with antioxidant, anti-inflammatory, antimicrobial, and immunomodulatory properties that can be further exploited through microbe-driven fermentation processes. On this premise, this literature review aims to provide an informative summary of the most updated evidence on their interactions with skin commensals and pathogens and on their ability to manage inflammatory conditions and restore microbial biodiversity. The emerging research showcases the potential novel beneficial ingredients for addressing various skincare concerns and advancing the cosmeceutics field as well. Full article

The Avilés Canyon System (ACS) is located in the southern Bay of Biscay (northern Spain, Cantabrian Sea). It has been declared a Site of Community Importance (SCI: C ESZZ12003) within the Natura 2000 Network and recognized as a Vulnerable Marine Ecosystem (VME). This area is included in the North Atlantic Marine Subdivision (NAMD). The present study reviews ophiuroid fauna collected during the INDEMARES–ACS project and compares the new findings with previous studies using the Official Spanish Checklist (“Inventario Español de Especies Marinas”) to update our knowledge on the diversity and distribution of these species. During the surveys carried out within the LIFE + INDEMARES–Avilés Canyon System project (2010–2012), a total of 7413 specimens belonging to 45 ophiuroid species were collected from 50 stations in a depth range between 266 and 2291 m. The most frequent species was Ophiactis abyssicola (M. Sars, 1861). Comparing the identified species with public datasets, seven species should be considered as new records for NAMD: Ophiocten centobi Paterson, Tyler & Gage, 1982, Amphiura borealis (G.O. Sars, 1872), Amphiura fragilis Verrill, 1885, Ophiochondrus armatus (Koehler, 1907), Ophiosabine parcita (Koehler, 1906), Ophiophrixus spinosus (Storm, 1881), Ophiotreta valenciennesi (Lyman, 1879). Furthermore, one species has expanded its bathymetric range: Ophiosabine parcita (Koehler, 1906). Full article

The possibility of using the Digital Image Correlation (DIC) technique, along with Lamb wave analysis, was investigated in this study for damage detection and characterization of polymer carbon fiber (CFRP) composites with the help of numerical modeling. The finite element model (FEM) of the composite specimen with artificial damage was developed in ANSYS and validated by the results of full-field DIC strain measurements. A quantitative analysis of the damage detection capabilities of DIC structure surface strain measurements in the context of different defect sizes, depths, and orientation angles relative to the loading direction was conducted. For Lamb wave analysis, a 2D spatial-temporal spectrum analysis and FEM using ABAQUS software were conducted to investigate the interaction of Lamb waves with the different defects. It was demonstrated that the FEM updating procedure could be used to characterize damage shape and size from the composite structure surface strain field from DIC. DIC defect detection capabilities for different loadings are demonstrated for the CFRP composite. For the identification of any composite defect, its characterization, and possible further monitoring, a methodology based on initial Lamb wave analysis followed by DIC testing is proposed. Full article

Contrast-enhanced ultrasound (CEUS) is an advanced imaging technique that integrates conventional US with the intravenous injection of specific US contrast agents (UCAs), combining the non-invasiveness of US with the higher accuracy of contrast-enhanced imaging. In contrast with magnetic resonance imaging (MRI), computed tomography (CT) and cystoscopy, CEUS has few contraindications, and UCAs are non-nephrotoxic agents that can be safely used in patients with kidney failure. CEUS is a well-established method for the detection of liver lesions and for echocardiography, and its indications are expanding. The updated 2018 WFUMB-EFSUMB guidelines have added the urinary bladder under non-hepatic applications of CEUS. The technique is able to distinguish between benign tissue, such as clots or hematoma, and malignant lesions by perfusing the mass with contrast agent. Thanks to the different perfusion rates of the various layers of the bladder wall, CEUS is also able to predict tumor invasion depth and stage. Despite that, current urological guidelines do not include CEUS as a plausible imaging technique for bladder urothelial carcinoma. The main reason for this omission might be the presence of scarce randomized evidence and the absence of large validated series. In this review, we describe the rationale behind the use of CEUS in bladder cancer and the added value of this imaging technique in the detection and staging of bladder lesions. In addition, we researched the available literature on the topic and then described the results of randomized clinical trials and a meta-analysis investigating the accuracy of CEUS in bladder cancer diagnosis and staging. The reported studies show that CEUS is a highly accurate diagnostic and staging tool for BC, reaching levels of specificity and sensitivity in differentiating between Ta-T1, or low-grade BC, and T2, or high-grade BC, that are comparable to those shown by the reference standard methods. Nonetheless, several limitations were found and are highlighted in this review. The aim of this study is to further validate and promote the use of CEUS as a quick, economic and effective diagnostic tool for this high-impact disease. Full article