Search Results (1,280)

Wastewater analysis plays a central role in monitoring patterns of drug use within specific populations. It provides objective and real-time estimates of consumption, with minimal ethical concerns. In the current European context, drugs of abuse continue to be detected in wastewater, with varying incidences across countries. Their monitoring enables the prioritisation of public health and legal interventions by healthcare professionals and drug monitoring agencies. Therefore, the development and implementation of efficient methodologies for monitoring drugs of abuse in wastewater samples is of critical importance. This systematic review aims to explore the use of miniaturised sample extraction techniques based on solid-phase microextraction for the determination of drugs of abuse in wastewater. In fact, the extraction procedure must be fast, effective, and selective in order to retain the analytes of interest. Miniaturised techniques have thus emerged as promising alternatives to conventional methods. Magnetic solid-phase extraction (MSPE) and molecularly imprinted polymers (MIPs) represent the most widely applied solid-phase microextraction techniques in recent years for the analysis of drugs of abuse in wastewater. Looking ahead, future perspectives include the development of eco-friendly workflows, automated and time-efficient techniques, increasingly selective sorbents, and robust analytical methods. Full article

Among the main diseases affecting dairy cattle is mastitis, a pathology widely recognized worldwide for causing considerable economic losses for both producers and the dairy industry. The conventional treatment involves the use of antibiotics, for which bacterial resistance has been reported. This fact has created the need to propose alternative treatments for this disease. Among the bacterial microorganisms associated with bovine mastitis are Streptococcus spp. and coagulase-positive Staphylococcus, which were isolated from milk obtained from cattle with mastitis in different dairy farms in the sector of Anaime in Cajamarca, Tolima. The objective of this research was to provide information on the antibacterial activity, toxicity, and phytochemical study (by TLC) of the following five plants—Calendula officinalis L., Psidium guajava L., Matricaria chamomilla L., Rosmarinus officinalis L., and Carica papaya L.—cultivated in Tolima, Colombia, with ethnopharmacological information in the treatment of diseases of bacterial origin. The ethanolic extracts of the selected species were obtained by maceration and were characterized for the presence of flavonoids by TLC. The antibacterial activity was evaluated in vitro using the Kirby-Bauer disk diffusion technique in Mueller–Hinton agar against Streptococcus spp. and coagulase-positive Staphylococcus strains. Ethanolic extracts of Psidium guajava L. (21 ± 3.2) and Rosmarinus officinalis L. (19 ± 2.1) showed the best activity against coagulase-positive Staphylococcus. In addition, ethanolic extracts of Calendula officinalis L. (21 ± 1.9), Rosmarinus officinalis L. (17 ± 2.9 mm), and Psidium guajava L. (15 ± 2.3) were the most active against Streptococcus spp. In contrast, the ethanolic extract of Matricaria chamomilla L. showed no activity against the strains evaluated. All extracts showed toxicity against Artemia salina nauplii at 24 h. It is important to mention that flavonoids were detected using TLC in all the extracts, which may be associated with antibacterial activity. Full article

This paper aims to unveil the symmetry–asymmetry transition mechanisms in transient fault waveforms of offshore wind power AC/DC transmission systems, addressing the critical limitation of traditional simulation methods of the fact that they cannot characterize the dynamic evolution of system symmetry, such as static impedance adjustment failing to capture transient asymmetry caused by parameter imbalance or converter control. It proposes a fault waveform simulation approach integrating mechanism analysis, scenario extraction, and model optimization. Key contributions include clarifying the quantitative links between key system parameters like submarine cable capacitance and inductance and symmetry–asymmetry characteristics, defining the transient decay rate oscillation frequency and voltage peak as core indicators to quantify symmetry breaking intensity; classifying typical fault scenarios into a symmetry-breaking type with synchronous three-phase imbalance and a persistent asymmetry type with zero-sequence and negative-sequence distortion based on symmetry evolution dynamics and revising grid-connection test indices such as lowering the low-voltage ride-through threshold and specifying the voltage type for different test objectives; and constructing a simplified embedded RLC second-order model with symmetry–asymmetry constraints to reproduce the whole process of symmetric steady state–fault symmetry breaking–recovery symmetry reconstruction. Simulation results verify the method’s effectiveness, with symmetry indicator reproduction errors ≤ 5% and asymmetric feature fitting goodness R² ≥ 0.92, which confirms that the method can effectively reveal the symmetry–asymmetry mechanisms of offshore wind power fault transients and provides reliable technical support for improving offshore wind power fault simulation accuracy and grid-connection test reliability, laying a theoretical basis for the grid-connection testing of offshore wind turbines and promoting the stable operation of offshore wind power systems. Full article

European cities are increasingly turning to data-driven solutions to tackle the complex challenges of urban mobility, yet many still lack high-resolution, multimodal data to make fact-based interventions. This paper presents the aims and initial findings of large-scale drone-based experiments conducted across five European cities—Athens, Madrid, Mykonos, Limassol, and Helsinki. Designed in close collaboration with local stakeholders, each deployment targeted city-specific objectives ranging from traffic congestion and safety to changing multimodal behaviour. Using GDPR-compliant computer vision techniques, we extracted privacy-preserving trajectory data that reveal detailed insights into flow dynamics, modal interactions, and behavioural patterns. Around 1.5 million trajectories were extracted in total. This paper offers a comparative analysis of findings across contexts and key lessons around stakeholder engagement, operational scalability, and ethical data practices. Our results demonstrate the potential of drone-based mobility monitoring as a powerful, flexible tool for supporting sustainable and inclusive urban transport planning across Europe. Full article

Development of new sustainable pesticides represents a real challenge for researchers due to environmental issues and public health aspects. In fact, the overuse of chemical pesticides has led to environmental damage, loss of biodiversity, and pesticide-resistant pests. In a framework characterized by the necessity of new sustainable agricultural practices, this study investigates the plant Genista ulicina as a producer of bioactive compounds for potential application as eco-friendly biopesticides. First, both roots and aerial parts of G. ulicina were extracted and the main compounds in the crude extracts were identified via GC-MS. Subsequently, the crude extracts were submitted to antifungal and phytotoxic assays. In particular, the antifungal effects were evaluated on three common phytopathogenic fungi, Fusarium oxysporum, Alternaria alternata, and Botrytis cinerea, while phytotoxic activity was evaluated on two weed species: Euphorbia peplus L. and Oxalis corniculata L. Further insights were obtained on the herbicidal potential of phytochemical compounds produced by G. ulicina through in silico investigations. In particular, molecular docking analyses were performed against three key enzymes involved in essential plant metabolic pathways: acetohydroxyacid synthase (AHAS), 4-hydroxyphenylpyruvate dioxygenase (HPPD), and protoporphyrinogen oxidase (PPO). Among the compounds identified, linolelaidic acid methyl ester, 1-monolinolein, stearic acid, and palmitic acid derivatives showed promising binding affinities and favorable interaction patterns compared to reference ligands. Selected phytochemicals from G. ulicina show potential as inhibitors of key herbicide targets, suggesting their value as promising leads in the development of sustainable bio-based weed control agents. Full article

In this study, we isolated and identified six major phenolic constituents from Cephalaria uralensis. The compounds—quercetin 6-C-β-glucopyranoside, isoorientin, swertiajaponin, 3,5-dicaffeoylquinic acid, 4,5-dicaffeoylquinic acid, and chlorogenic acid—were characterized by LC–MS and NMR. All isolates exhibited strong free-radical scavenging ability and significant interaction with lipid monolayers (Δπ up to ~6.5–7 mN/m), suggesting dual antioxidant and membrane-perturbing activities. In antioxidant assays, isoorientin, showed the lowest IC₅₀ among the isolates. Notably, 4,5-dicaffeoylquinic acid caused the largest increase in monolayer surface pressure, indicating a particularly strong tendency to integrate with lipid bilayers. In fact, chlorogenic acid, isoorientin, and swertiajaponin are well-documented natural antioxidants, and related phenolic acids have been shown to possess potent antimicrobial activity. Thus, the C. uralensis phenolics identified in our study likely underlie the extract’s bioactivity. These findings highlight C. uralensis as a source of membrane-active polyphenols with potential applications in skin-related oxidative and microbial conditions. Full article

The growth of e-government has rendered automated element extraction from official documents a critical bottleneck for administrative efficiency. The core challenge lies in unifying deep semantic understanding with the structured domain knowledge required to interpret complex formats and specialized terminology. To address the limitations of existing methods, we propose a hybrid framework. Our approach leverages a BERT-CRF model for robust sequence labeling, a knowledge graph (KG)-driven retrieval system to ground the model in verifiable facts, and a large language model (LLM) as a reasoning engine to resolve ambiguities and identify complex relationships. Validated on the DovDoc-CN dataset, our framework achieves a macro-average F1 score of 0.850, outperforming the BiLSTM-CRF baseline by 2.41 percentage points, and demonstrates high consistency, with a weighted F1 score of 0.984. The low standard deviation in the validation set further indicates the model’s stable performance across different subsets. These results confirm that our integrated approach provides an efficient and reliable solution for intelligent document processing, effectively handling the format diversity and specialized knowledge characteristic of government documents. Full article

Buckwheat (Fagopyrum tataricum) is native to Yunnan, China, and as a miscellaneous grain crop with high nutritional value, it has received increased attention from farmers and enterprises in recent years. In June 2024, we observed severe anthracnose in the buckwheat cultivation area in Malu Township and Jiache Township, Huize County, Qujing City, Yunnan Province, China. In this study, six isolates (SM01–SM06) of anthracnose with similar morphology were obtained using the tissue isolation method, which was due to the fact that this disease is highly pathogenic to buckwheat. The strain SM02 was selected as a representative isolate for biological characterization and molecular phylogenetic analysis, and a phylogenetic tree was constructed based on the ACT, CHS, and ITS genes to determine its taxonomic status. The selected SM02 isolate was further identified as Colletotrichum kahawae. Biological characterization showed that the representative strain SM02 exhibited optimal growth for in vitro cultivation under a photoperiod, temperature, pH, carbon source, and nitrogen source of 12L:12D, 25 °C, pH 7.0, glucose, and beef extract, respectively. Host range testing demonstrated that C. kahawae might infect important field crops, including maize, wheat, oats, and potatoes. In conclusion, C. kahawae causes buckwheat anthracnose in China, which might hinder the production of buckwheat. This study provides insight into anthracnose disease in buckwheat and provides a basis for further investigations to assess and implement effective disease management strategies. Full article

The construction of knowledge graphs in cyber threat intelligence (CTI) critically relies on automated entity–relation extraction. However, sequence tagging-based methods for joint entity–relation extraction are affected by the order-dependency problem. As a result, overlapping relations are handled ineffectively. To address this limitation, a parallel, ensemble-prediction–based model is proposed for joint entity–relation extraction in CTI. The joint extraction task is reformulated as an ensemble prediction problem. A joint network that combines Bidirectional Encoder Representations from Transformers (BERT) with a Bidirectional Gated Recurrent Unit (BiGRU) is constructed to capture deep contextual features in sentences. An ensemble prediction module and a triad representation of entity–relation facts are designed for joint extraction. A non-autoregressive decoder is employed to generate relation triad sets in parallel, thereby avoiding unnecessary sequential constraints during decoding. In the threat intelligence domain, labeled data are scarce and manual annotation is costly. To mitigate these constraints, the SecCti dataset is constructed by leveraging ChatGPT’s small-sample learning capability for labeling and augmentation. This approach reduces annotation costs effectively. Experimental results show a 4.6% absolute F1 improvement over the baseline on joint entity–relation extraction for threat intelligence concerning Advanced Persistent Threats (APTs) and cybercrime activities. Full article

Most astrophysical systems (except for very compact objects such as, e.g., black holes and neutron stars) in our Universe are characterized by shallow gravitational potentials, with dimensionless compactness $|\Phi |\equiv r_s/R\ll 1$, where $r_s$ and R are their Schwarzschild radius and typical size, respectively. While the existence and characteristic scales of such virialized systems depend on gravity, we demonstrate that the value of $|\Phi |$—and thus the non-relativistic nature of most astrophysical objects—arises from microphysical parameters, specifically the fine structure constant and the electron-to-proton mass ratio, and is fundamentally independent of the gravitational constant, G. In fact, the (generally extensive) gravitational potential becomes ‘locally’ intensive at the system boundary; the compactness parameter corresponds to the binding energy (or degeneracy energy, in the case of quantum degeneracy pressure-supported systems) per proton, representing the amount of work that needs to be done in order to allow proton extraction from the system. More generally, extensive properties of gravitating systems depend on G, whereas intensive properties do not. It then follows that peak rms values of large-scale astrophysical velocities and escape velocities associated with naturally formed astrophysical systems are determined by electromagnetic and atomic physics, not by gravitation, and that the compactness, $|\Phi |$, is always set by microphysical scales—even for the most compact objects, such as neutron stars, where $|\Phi |$ is determined by quantities like the pion-to-proton mass ratio. This observation, largely overlooked in the literature, explains why the Universe is not dominated by relativistic, compact objects and connects the relatively low entropy of the observable Universe to underlying basic microphysics. Our results emphasize the central but underappreciated role played by dimensionless microphysical constants in shaping the macroscopic gravitational landscape of the Universe. In particular, we clarify that this independence of the compactness, $|\Phi |$, from G applies specifically to entire, virialized, or degeneracy pressure-supported systems, naturally formed astrophysical systems—such as stars, galaxies, and planets—that have reached equilibrium between self-gravity and microphysical processes. In contrast, arbitrary subsystems (e.g., a piece cut from a planet) do not exhibit this property; well within/outside the gravitating object, the rms velocity is suppressed and G reappears. Finally, we point out that a clear distinction between intensive and extensive astrophysical/cosmological properties could potentially shed new light on the mass hierarchy and the cosmological constant problems; both may be related to the large complexity of our Universe. Full article

Traditional GCN based methods capture the propagation structure between posts, but do not fully model dynamic semantic information, such as the role of specific users on the propagation path and the context of post content that changes over time, leading to a decrease in the accuracy of rumor detection. Therefore, we propose an innovative path attention graph convolution network (PAGCN) framework, which effectively solves this limitation by integrating propagation structure and semantic representation learning. PAGCN first uses the graph neural network (GNN) to model the information transmission path, focusing on the differences between rumor and fact information in communication behavior, such as the differences between depth first and breadth first dissemination modes. Then, in order to enhance the ability of semantic understanding, we design a multi head attention mechanism based on convolutional neural network (CNN), which extracts deep contextual relationships from text content. Furthermore, by introducing the comparative learning technology, PAGCN can adaptively optimize the representation of structural and semantic features, dynamically focus on the most discriminative features, and significantly improve the sensitivity to subtle patterns in rumor propagation. The experimental verification on three benchmark datasets of twitter15, twitter16, and Weibo, shows that the proposed PAGCN performs best among the 17 comparison models, and the accuracy rates on twitter15 and Weibo datasets are 90.9% and 93.9%, respectively, which confirms the effectiveness of the framework in capturing propagation structure and semantic information at the same time. Full article

Food product labels serve as a critical source of information, providing details about nutritional content, ingredients, and health implications. These labels enable Food and Drug Authorities (FDA) to ensure compliance and take necessary health-related and logistics actions. Additionally, product labels are essential for online grocery stores to offer reliable nutrition facts and empower customers to make informed dietary decisions. Unfortunately, product labels are typically available in image formats, requiring organizations and online stores to manually transcribe them—a process that is not only time-consuming but also highly prone to human error, especially with multilingual labels that add complexity to the task. Our study investigates the challenges and effectiveness of leveraging large language models (LLMs) to extract nutritional elements and values from multilingual food product labels, with a specific focus on Arabic and English. A comprehensive empirical analysis was conducted using a manually curated dataset of 294 food product labels, comprising 588 transcribed nutritional elements and values in both languages, which served as the ground truth for evaluation. The findings reveal that while LLMs performed better in extracting English elements and values compared to Arabic, our post-processing techniques significantly enhanced their accuracy, with GPT-4o outperforming GPT-4V and Gemini. Full article

Etching has become a critical step in semiconductor wafer fabrication, and its importance in semiconductor manufacturing highlights the fact that it directly determines the ability of the fab to produce high-process products, as well as the application performance of the chip. While the health of the etcher is a concern, especially for the cooling system, accurately predicting the remaining useful life (RUL) of the etcher cooling system is a critical task. Predictive maintenance (PDM) can be used to monitor the basic condition of the equipment by learning from historical data, and it can help solve the task of RUL prediction. In this paper, we propose the FECAM-WTCN-Informer model, which first obtains a new WTCN structure by inserting wavelet convolution into the TCN, and then combines the discrete cosine transform (DCT) and channel attention mechanism into the temporal neural network (TCN). Multidimensional feature extraction of time series data can be realized, and the processed features are input into the Informer network for prediction. Experimental results show that the method is significantly more accurate in terms of overall prediction performance (MSE, RMSE, and MAE), compared with other state-of-the-art methods, and is suitable for solving the problem of predictive maintenance of etching machine cooling systems. Full article

This research demonstrates the potential of plant waste cellulose as a remarkable biomaterial for multilayer tablet formulation. Rice husks (RC) and orange peels (OC) were used as cellulose sources and characterized for a comparison with commercial cellulose. The FTIR characterization shows minimal differences in their chemical components, making them equivalent for compression into tablets containing ibuprofen. TGA measurements indicate that the RC is slightly better for multilayer formulations due to its favorable degradation profile. This is corroborated by an XRD analysis that reveals its higher crystalline fraction (~55%). The use of a heat press at combined high pressures and temperatures allows the layer-by-layer tablet formulation of ibuprofen, taken as a model drug. Additionally, this study compares the release profile of three types of tablets compressed with cellulose: mixed (MIX), two-layer (BL), and three-layer (TL). The MIX tablet shows a profile like that of conventional ibuprofen tablets. Although both BL and TL tablets significantly reduce their release percentage in the first hours, the TL ones have proven to be better in the long run. In fact, formulations made of extracted cellulose sandwiching ibuprofen display a zero-order release profile and prolonged release since the drug release amounts to ~70% after 120 h. This makes the TL formulations ideal for maintaining the therapeutic effect of the drug and improving patients’ wellbeing and compliance while reducing adverse effects. Full article

Machine learning techniques have revolutionized physical layer security (PLS) and provided opportunities for optimizing the performance and security of modern communication systems. In this study, we propose the first machine learning-assisted random communication system (ML-RCS). It comprises a pretrained decision tree (DT)-based receiver that extracts binary information from the transmitted random noise carrier signals. The ML-RCS employs skewed alpha-stable ($\alpha$-stable) noise as a random carrier to encode the incoming binary bits securely. The DT model is pretrained on an extensively developed dataset encompassing all the selected parameter combinations to generate and detect the $\alpha$-stable noise signals. The legitimate receiver leverages the pretrained DT and a predetermined key, specifically the pulse length of a single binary information bit, to securely decode the hidden binary bits. The performance evaluations included the single-bit transmission, confusion matrices, and a bit error rate (BER) analysis via Monte Carlo simulations. The fact that the BER reached 10⁻³ confirms the ability of the proposed system to establish successful secure communication between a transmitter and legitimate receiver. Additionally, the ML-RCS provides an increased data rate compared to previous random communication systems. From the perspective of security, the confusion matrices and computed false negative rate of 50.2% demonstrate the failure of an eavesdropper to decode the binary bits without access to the predetermined key and the private dataset. These findings highlight the potential ability of unconventional ML-RCSs to promote the development of secure next-generation communication devices with built-in PLSs. Full article