Search Results (512)

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

The development of generative AI Large Language Models (LLMs) raised the alarm regarding the identification of content produced by generative AI vs. humans. In one case, issues arise when students heavily rely on such tools in a manner that can affect the development of their writing or coding skills. Other issues of plagiarism also apply. This study aims to support efforts to detect and identify textual content generated using LLM tools. We hypothesize that LLM-generated text is detectable by machine learning (ML) and investigate ML models that can recognize and differentiate between texts generated by humans and multiple LLM tools. We used a dataset of student-written text in comparison with LLM-written text. We leveraged several ML and Deep Learning (DL) algorithms, such as Random Forest (RF) and Recurrent Neural Networks (RNNs) and utilized Explainable Artificial Intelligence (XAI) to understand the important features in attribution. Our method is divided into (1) binary classification to differentiate between human-written and AI-generated text and (2) multi-classification to differentiate between human-written text and text generated by five different LLM tools (ChatGPT, LLaMA, Google Bard, Claude, and Perplexity). Results show high accuracy in multi- and binary classification. Our model outperformed GPTZero (78.3%), with an accuracy of 98.5%. Notably, GPTZero was unable to recognize about 4.2% of the observations, but our model was able to recognize the complete test dataset. XAI results showed that understanding feature importance across different classes enables detailed author/source profiles, aiding in attribution and supporting plagiarism detection by highlighting unique stylistic and structural elements, thereby ensuring robust verification of content originality. Full article

The 2 × 25 kV flexible traction power supply system (FTPSS), using a three-phase-single-phase converter as its power source, effectively addresses the challenges of neutral section transitions and power quality issues inherent in traditional power supply systems (TPSSs). However, the bidirectional fault current and low short-circuit current characteristics degrade the effectiveness of traditional TPSS protection schemes. This paper analyzes the fault characteristics of FTPSS and proposes a fault identification method based on empirical wavelet transform (EWT) and 1-sequence faulty energy. First, a composite sequence network model is developed to reveal the characteristics of three typical fault types, including ground faults and inter-line short circuits. The 1-sequence differential faulty energy is then calculated. Since the 1-sequence component is unaffected by the leakage impedance of autotransformers (ATs), the proposed method uses this feature to distinguish the TPSS faults from disturbances caused by electric multiple units (EMUs). Second, EWT is used to decompose the 1-sequence faulty energy, and relevant components are selected by permutation entropy. The fault variance derived from these components enables reliable identification of TPSS faults, effectively avoiding misjudgment caused by AT excitation inrush or harmonic disturbances from EMUs. Finally, real-time digital simulator experimental results verify the effectiveness of the proposed method. The fault identification method possesses high tolerance to transition impedance performance and does not require synchronized current measurements from both sides of the TPSS. Full article

With the increasing switching frequencies and power densities in modern power converters, the prediction and mitigation of common-mode (CM) noise are becoming increasingly essential. Even though powerful, simulation methods are hindered by the difficulties in modeling power semiconductors and the long simulation time. As an alternative, the measurement-based substitution model is demonstrated in the paper, which simplifies the non-linear converter with a linear circuit network with multiple independent sources. Transfer functions are then defined and characterized to evaluate the conversion ratio from different sources to the CM noise produced on the attached cables. Good agreements are observed between the predicted and measured CM noise under several test conditions. Additionally, the proposed method facilitates dominant noise source identification and the corresponding noise suppression. The proposed method offers advantages over the existing approach, including simplicity in the characterization of transfer functions and the least disturbance to the test setup. Full article

Dry-cured ham is a traditional food in the Mediterranean diet, which, in addition to its sensory qualities, is a natural source of bioactive peptides generated during the curing process through the action of endogenous enzymes on muscle and sarcoplasmic proteins. These low-molecular-weight peptides have attracted growing interest due to their multiple bioactivities, including antihypertensive, antioxidant, antimicrobial, antidiabetic, and anti-inflammatory effects described in vitro, in vivo, and in preliminary human studies. The identification of specific sequences, such as AAPLAP, KPVAAP, and KAAAAP (ACE inhibitors), SNAAC and GKFNV (antioxidants), RHGYM (antimicrobial), and AEEEYPDL and LGVGG (dipeptidyl peptidase-IV and α-glucosidase inhibitors), has been possible thanks to the use of peptidomics techniques, tandem mass spectrometry, and bioinformatics tools that allow their activity to be characterized, their digestive stability to be predicted, and their bioavailability to be evaluated. This review article summarizes current knowledge on the bioactivities of peptides derived from dry-cured ham, advances in their functional characterization, and challenges associated with their application in functional foods and nutraceuticals, with the aim of providing a comprehensive overview of their potential in health promotion and chronic disease prevention. Full article

The reconstruction of historic buildings requires the integration of diverse data sources, both geometric and non-geometric. This study presents a multi-source data analysis methodology for heritage reconstruction using 3D modeling and Historic Building Information Modeling (HBIM). The proposed approach combines geometric data, including point clouds acquired via Terrestrial Laser Scanning (TLS), with architectural documentation and non-geometric information such as photographs, historical records, and technical descriptions. The case study focuses on a wooden Orthodox church in Żmijowiska, Poland, analyzing geometric changes in the structure over multiple decades. The reconstruction process integrates modern surveys with archival sources and, in the absence of complete geometric data, utilizes semantic, topological, and structural information. Geometric datasets from the 1990s, 1930s, and the turn of the 20th century were analyzed, supplemented by intermediate archival photographs and technical documentation. This integrated method enabled the identification of transformation phases and verification of discrepancies between historical records and the building’s actual condition. The findings confirm that the use of HBIM and multi-source data fusion facilitates accurate reconstruction of historical geometry and supports visualization of spatial changes across decades. Full article

Considering the problems of having insufficient fault identification from single information sources in actual industrial environments, and different information sensitivity in multi-information source data, and different sensitivity of artificial feature extraction, which can lead to difficulties of effective fusion of equipment information, insufficient state representation ability, low fault identification accuracy, and poor robustness, a multi-information fusion fault identification network model based on deep ensemble learning is proposed. The network is composed of multiple sub-feature extraction units and feature fusion units. Firstly, the fault feature mapping information of each information source is extracted and stored in different sub-models, and then, the features of each sub-model are fused by the feature fusion unit. Finally, the fault recognition results are obtained. The effectiveness of the proposed method is evaluated by using two gearbox datasets. Compared with the method of simple stacking fusion and single measuring point without fusion, the accuracy of each type of fault recognition of the proposed method is close to 100%. The results show that the proposed method is feasible and effective in the application of gearbox fault recognition. Full article

This paper presents a procedural method aimed at protecting maritime critical infrastructure, which is essential for the functioning of developed nations. A novel approach, developed by the authors, is introduced—focusing on the behavioral analysis of vessels to enable early identification of suspicious maritime activity and to prevent damage or destruction to key infrastructure elements. An integrated system is proposed, combining real-time electronic surveillance with continuous access to and analysis of data from both national and international databases. Drawing inspiration from medical sciences, a screening-based methodology has been developed. Data on vessels collected from various sources are processed according to the criteria adopted by the authors, using a multi-criteria decision analysis (MCDA) approach. MCDA is a decision-support method that considers multiple criteria simultaneously. It allows for the comparison and evaluation of different options, even when they are difficult to compare directly. This characteristic is used to select high-risk vessels for further monitoring. An initial classification of a vessel as suspicious does not constitute proof of criminal activity but rather serves as a trigger for further coordinated actions. Data on vessels is collected from the AIS (automatic identification system) and platforms that store vessel history. The AIS is a powerful tool that processes parameters such as a ship’s speed and course. This article presents sample results from surveillance and pre-selection analyses using the AIS, followed by a multi-criteria assessment of the behavior of vessels identified through this process. The results are presented both graphically and numerically. The authors conducted several scenarios, analyzing different groups of vessels. Based on this analysis, recommendations were developed for the interpretation of the findings. Full article

Magnetic parameters of river sediments are commonly used as end-members for source tracing in the coasts and shelves. The eastern continental shelf area of China, with multiple sources of input, is a key region for discussing sediment sources. However, magnetic parameters are influenced by grain size, and the nature of this influence remains unclear. In this study, the Jiulong River was selected as a case to analyze the magnetic parameters and mineral characteristics for both the coarse (>63 μm) and fine-grained (<63 μm) fractions. Results show that the magnetic minerals mainly contain detrital-sourced magnetite and hematite. In the North River, a tributary of the Jiulong River, the content of coarse-grained magnetic minerals increases from upstream to downstream, contrary to fine-grained magnetic minerals, suggesting the influence of hydrodynamic forces. Some samples with abnormally high magnetic susceptibility may result from the combined influence of the parent rock and human activities. In the scatter diagrams of magnetic parameters for provenance tracing, samples of the <63 μm fractions have a more concentrated distribution than that of the >63 μm fractions. Hence, magnetic parameters for the <63 μm fraction are more useful in provenance identification. Full article

This paper introduces PyBEP, a Python-based tool for the automated and optimized selection of open-circuit potential (OCP) curves and calculation of stoichiometric cycling ranges for lithium-ion battery electrodes based on open-circuit voltage (OCV) measurements. Thereby, it overcomes key challenges in traditional approaches, which are often time-intensive and susceptible to errors due to manual curve digitization, data inconsistency, and coding complexities. The originality of PyBEP arises from the systematic integration of automated electrode chemistry identification, quality-controlled database usage, refinement of the results using incremental capacity methodology, and simultaneous optimization of multiple electrode parameters. The PyBEP database leverages high-quality, curated OCP data and employs differential evolution optimization for precise OCP determination. Validation against literature data and experimental results confirms the robustness and accuracy of PyBEP, consistently achieving precision of 10 mV or better. PyBEP also offers features like electrode chemical composition identification and quality enhancement of measurement data, further extending the battery modeling functionalities without the need for battery disassembly. PyBEP is open-source and accessible on GitHub, providing a streamlined, accurate resource for the battery research community, making PyBEP a unique and directly applicable toolkit for electrochemical researchers and engineers. Full article

The construction and maintenance of an ecological security pattern (ESP) are important for promoting the regional development of ecological civilizations, realizing sustainable and healthy development, and creating a harmonious and beautiful space for human beings and nature to thrive. Traditional construction methods have the limitations of a single dimension, a single method, and excessive human subjective intervention for source and corridor identification, without considering the multidimensional quality of the sources and the structural connectivity and resilience optimization of the corridors. Therefore, an ecological civilization demonstration area (Jiangxi Province) was used as the study area, a new research method for ESP was proposed, and an empirical study was conducted. To evaluate ecosystem service (ES) importance–disturbance–risk and extract sustainability sources through the deep embedded clustering–self-organizing map (DEC–SOM) deep unsupervised learning clustering algorithm, ecological networks (ENs) were constructed by applying the minimum cumulative resistance (MCR) gravity model and circuit theory. The ENs were then optimized to improve performance by combining the comparative advantages of the two approaches in terms of structural connectivity and resilience. A comparative analysis of EN performance was constructed among different functional control zones, and the ESP was constructed to include 42 ecological sources, 134 corridors, 210 restoration nodes, and 280 protection nodes. An ESP of ‘1 nucleus, 3 belts, 6 zones, and multiple corridors’ was constructed, and the key restoration components and protection functions were clarified. This study offers a valuable reference for ecological management, protection, and restoration and provides insights into the promotion of harmonious symbiosis between human beings and nature and sustainable regional development. Full article

Per- and polyfluoroalkyl substances (PFASs) are emerging pollutants of global concern due to their high environmental persistence and bioaccumulative characteristics. This study investigates PFAS concentrations in soils from China through an extensive literature review, covering soil samples from seventeen provinces and the years from 2009 to 2024. It was found that the total concentration of PFAS in soil ranged from 0.25 to 6240 ng/g, with the highest contamination levels observed in coastal provinces, particularly Fujian (620 ng/g) and Guangdong (1090 ng/g). Moreover, Fujian Province ranked the highest among multiple regions with a median PFAS concentration of 15.7 ng/g for individual compounds. Ecological risk assessment, focusing on areas where perfluorooctanoic acid (PFOA) or perfluorooctane sulfonate (PFOS) were identified as the primary soil PFAS compounds, showed moderate ecological risk from PFOA in Shanghai (0.24), while PFOS posed a high ecological risk in Fujian and Guangdong, with risk values of 43.3 and 1.4, respectively. Source analysis revealed that anthropogenic activities, including PFAS production, firefighting foam usage, and landfills, were the primary contributors to soil contamination. Moreover, soil PFASs tend to migrate into groundwater via adsorption and seepage, ultimately entering the human body through bioaccumulation or drinking water, posing health risks. These findings enhance our understanding of PFAS distribution and associated risks in Chinese soils, providing crucial insights for pollution management, source identification, and regulation strategies in diverse areas. Full article

Peptides play a crucial role in the development of pharmaceuticals and functional foods. Multiple studies have shown that natural bioactive peptides possess antioxidant, antihypertensive, anti-tumor, and anti-inflammatory activities. Marine bioactive peptides, especially those sourced from fish, constitute a substantial reservoir of these molecules. Although considerable research has been undertaken on fish-derived peptides, studies specifically concerning those from tuna are limited. Tuna, a marine fish of high nutritional value, generates substantial by-product waste during fishing and processing. Therefore, it is essential to conduct an evaluation of the advancements in study on tuna-derived active peptides and to offer a perspective on the direction of future investigations. This review integrates prospective bioactive peptides derived from tuna and reports contemporary strategies for their investigation, including extraction, purification, screening, identification, and activity evaluation procedures, including Yeast Surface Display (YSD) and molecular docking. This review seeks to promote the continued investigation and application of bioactive peptides derived from tuna. Full article

Intrusion detection systems are crucial for cybersecurity applications. Network traffic data originate from diverse terminal sources, exhibiting multi-view feature spaces, while the collection of unknown intrusion data is costly. Current one-class classification (OCC) approaches are mainly designed for single-view data. Multi-view OCC approaches usually require collecting multi-view traffic data from all sources and have difficulty detecting intrusion independently in each view. Furthermore, they commonly ignore the potential subcategories in normal traffic data. To address these limitations, this paper utilizes the Broad Learning System (BLS) technique and proposes an intrusion detection framework based on a multi-view cluster structure guided one-class BLS-autoencoder (IDF-MOCBLSAE). Specifically, a multi-view co-association matrix optimization objective function with doubly-stochastic constraints is first designed to capture the cross-view cluster structure. Then, a multi-view cluster structure guided one-class BLS-autoencoder (MOCBLSAEs) is proposed, which learns the discriminative patterns of normal traffic data by preserving the cross-view clustering structure while minimizing the intra-view sample reconstruction errors, thereby enabling the identification of unknown intrusion data. Finally, an intrusion detection framework is constructed based on multiple MOCBLSAEs to achieve both individual and ensemble intrusion detection. Through experimentation, IDF-MOCBLSAE is validated on real-world network traffic datasets for multi-view one-class classification tasks, demonstrating its superiority over state-of-the-art one-class approaches. Full article

This study presents a comprehensive methodology for the detection and characterization of fractures in geological samples using X-ray computed tomography (CT). By combining convolution-based image processing techniques with advanced neural network-based segmentation, the proposed approach achieves high precision in identifying complex fracture networks. The method was applied to a marly limestone sample from the Maiolica Formation, part of the Umbria–Marche stratigraphic succession (Northern Apennines, Italy), a geological context where fractures often vary in size and contrast and are frequently filled with minerals such as calcite or clays, making their detection challenging. A critical part of the work involved addressing multiple sources of uncertainty that can impact fracture identification and measurement. These included the inherent spatial resolution limit of the CT system (voxel size of 70.69 μm), low contrast between fractures and the surrounding matrix, artifacts introduced by the tomographic reconstruction process (specifically the Radon transform), and noise from both the imaging system and environmental factors. To mitigate these challenges, we employed a series of preprocessing steps such as Gaussian and median filtering to enhance image quality and reduce noise, scanning from multiple angles to improve data redundancy, and intensity normalization to compensate for shading artifacts. The neural network segmentation demonstrated superior capability in distinguishing fractures filled with various materials from the host rock, overcoming the limitations observed in traditional convolution-based methods. Overall, this integrated workflow significantly improves the reliability and accuracy of fracture quantification in CT data, providing a robust and reproducible framework for the analysis of discontinuities in heterogeneous and complex geological materials. Full article