Search Results (864)

Clinical manifestations of salmonellosis in humans typically include acute gastroenteritis, abdominal pain, diarrhea, nausea, and fever. Diarrhea and anorexia may persist for several days. In some cases, the organisms may invade the intestinal mucosa and cause septicemia, even in the absence of significant gastrointestinal symptoms. Most clinical signs are attributed to hematogenous dissemination of the pathogen. As with other microbial infections, disease severity is influenced by the serotype of the organism, bacterial load, and host susceptibility. Serotyping analysis of Salmonella spp. using the White–Kauffmann–Le Minor scheme remains the gold standard for strain typing. However, this method is expensive, time-consuming, and requires significant expertise and visual interpretation by trained personnel, which is why it is typically restricted to regional or national reference laboratories. In this study, we evaluated a spectroscopic technique coupled with chemometrics and multivariate machine learning algorithms for its ability to discriminate the main Salmonella spp. serogroups in a clinical routine setting. We analyzed 95 isolates of Salmonella that were randomly selected, including four strains of S. Typhi. The I-dOne Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) system (Alifax S.r.l., Polverara, Italy) also shows promising potential for distinguishing Salmonella Typhi within the D serogroup. The I-dOne system enables simultaneous identification of both species and subspecies using the same workflow and instrumentation, thus streamlining the diagnostic process. Full article

Food fraud in meat products presents serious economic and public health challenges, underscoring the need for rapid and reliable detection methods. This study investigates the potential of Raman spectroscopy combined with machine learning to accurately discriminate between pure and mixed minced meat preparations. We evaluated three classification algorithms: Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Random Forests (RFs). Raman spectra were collected from 19 distinct samples consisting of different ratios of pork, beef, and lamb minced meat. Our findings suggest that homogenization markedly enhances spectral consistency and classification accuracy. In the pure meat samples case, all three models (SVM, ANN, and RF) achieved notable increases in classification accuracies (from 0.50–0.70 to above 0.85), a dramatic improvement over unhomogenized samples. In more complex homogenized mixtures, SVM delivered the highest performance, achieving an accuracy of up to 0.88 for 50:50 mixtures and 0.86 for multi-ratio samples, often outperforming both ANN and RF. While the underlying interpretation of the classification models remains complex, the findings consistently underscore the critical role of homogenization on model performance. This work demonstrates the robust potential of the Raman spectroscopy-coupled machine learning approach for the rapid and accurate identification of minced meat species. Full article

Automated electroencephalography (EEG) seizure detection is meaningful in clinical medicine. However, current approaches often lack comprehensive feature extraction and are limited by generic classifier architectures, which limit their effectiveness in complex real-world scenarios. To overcome this traditional coupling between feature representation and classifier development, this study proposes DySC-MDE, an end-to-end co-designed framework for seizure detection. A novel multi-domain entropy (MDE) representation is constructed at the feature level based on amplitude-sensitive permutation entropy (ASPE), which adopts entropy-based quantifiers to characterize the nonlinear dynamics of EEG signals across diverse domains. Specifically, ASPE is extended into three distinct variants, refined composite multiscale ASPE (RCMASPE), discrete wavelet transform-based hierarchical ASPE (HASPE-DWT), and time-shift multiscale ASPE (TSMASPE), to represent various temporal and spectral dynamics of EEG signals. At the classifier level, a dynamic synapse classifier (DySC) is proposed to align with the structure of the MDE features. Particularly, DySC includes three parallel and specialized processing pathways, each tailored to a specific entropy variant. These outputs are then adaptively fused through a dynamic synaptic gating mechanism, which can enhance the model’s ability to integrate heterogeneous information sources. To fully evaluate the effectiveness of the proposed method, extensive experiments are conducted on two public datasets using cross-validation. For the binary classification task, DySC-MDE achieves an accuracy of 97.50% and 98.93% and an F1-score of 97.58% and 98.87% in the Bonn and CHB-MIT datasets, respectively. Moreover, in the three-class task, the proposed method maintains a high F1-score of 96.83%, revealing its strong discriminative performance and generalization ability across different categories. Consequently, these impressive results demonstrate that the joint optimization of nonlinear dynamic feature representations and structure-aware classifiers can further improve the analysis of complex epileptic EEG signals, which opens a novel direction for robust seizure detection. Full article

To unravel the link between agate geochemistry, host volcanic rocks, and ore-forming processes, this study integrated elemental correlation analysis, interaction interpretation, and interpretable machine learning (LightGBM-SHAP framework with SMOTE and 5-fold cross-validation) using 203 in-situ element datasets from 16 global deposits. The framework achieved 99.01% test accuracy and 97.4% independent prediction accuracy in discriminating host volcanic rock types. Key findings reveal divergence between statistical elemental correlations and geological interactions. Synergies reflect co-migration/co-precipitation, while antagonisms stem from source competition or precipitation inhibition, unraveling processes like stepwise crystallization. Rhyolite-hosted agates form via a “crust-derived magmatic hydrothermal fluid—medium-low salinity complexation—multi-stage precipitation” model, driven by high-silica fluids enriching Sb/Zn. Andesite-hosted agates follow a “contaminated fluid—hydrothermal alteration—precipitation window differentiation” model, controlled by crustal contamination. Basalt-hosted agates form through a “low-temperature hydrothermal fluid—basic alteration—progressive mineral decomposition” model, with meteoric water regulating Na-Zn relationships. Zn acts as a cross-lithology indicator, tracing crust-derived fluid processes in rhyolites, feldspar alteration intensity in andesites, and alteration timing in basalts. This work advances volcanic-agate genetic studies via “correlation—interaction—mineralization model” coupling, with future directions focusing on large-scale micro-area elemental analysis. Full article

This paper introduces a method for detecting weak line spectrum signals in dynamic, non-Gaussian marine noise using a single vector hydrophone. The trispectrum diagonal slice is employed to extract coupled line spectrum features, enabling the detection of line spectra with independent frequencies and phases while effectively suppressing Gaussian noise. By constructing a cross-trispectrum diagonal slice spectrum from the hydrophone’s sound pressure and composite particle velocity, the method leverages coherence gain to enhance the signal-to-noise ratio (SNR). Furthermore, a discriminator based on the cross-coherence function of pressure and velocity is proposed, which utilizes a dynamic threshold to adaptively and in real-time select either the vector cross-trispectrum diagonal slice (V-TriD) or the conventional energy detection (ED) as the optimal detection channel for incoming signal. The feasibility and effectiveness of this method were validated through simulations and sea trial data from the South China Sea. Experimental results demonstrate that the proposed algorithm can effectively detect the target signal, achieving an SNR improvement of 3 dB at the target frequency and an average reduction in broadband noise energy of 1–2 dB compared to traditional energy spectrum detection. The proposed algorithm exhibits computational efficiency, adaptability, and robustness, making it well suited for real-time underwater target detection in critical applications, including harbor security, waterway monitoring, and marine bioacoustic studies. Full article

With the rapid expansion of multi-platform remote sensing applications, cloud contamination significantly impedes cross-platform data utilization. Current cloud detection methods face critical technical challenges in cross-platform settings, including neglect of atmospheric radiative transfer mechanisms, inadequate multi-scale structural decoupling, high intra-class variability coupled with inter-class similarity, cloud boundary ambiguity, cross-modal feature inconsistency, and noise propagation in pseudo-labels within semi-supervised frameworks. To address these issues, we introduce a Physics-Guided Multi-Representation Network (PGMRN) that adopts a student–teacher architecture and fuses tri-modal representations—Pseudo-NDVI, structural, and textural features—via atmospheric priors and intrinsic image decomposition. Specifically, PGMRN first incorporates an InfoNCE contrastive loss to enhance intra-class compactness and inter-class discrimination while preserving physical consistency; subsequently, a boundary-aware regional adaptive weighted cross-entropy loss integrates PA-CAM confidence with distance transforms to refine edge accuracy; furthermore, an Uncertainty-Aware Quadruple Consistency Propagation (UAQCP) enforces alignment across structural, textural, RGB, and physical modalities; and finally, a dynamic confidence-screening mechanism that couples PA-CAM with information entropy and percentile-based thresholding robustly refines pseudo-labels. Extensive experiments on four benchmark datasets demonstrate that PGMRN achieves state-of-the-art performance, with Mean IoU values of 70.8% on TCDD, 79.0% on HRC_WHU, and 83.8% on SWIMSEG, outperforming existing methods. Full article

Forest disturbance is a major driver shaping the structure and function of plantation ecosystems. Current research predominantly focuses on single forest types or landscape scales. However, species-level fine-scale assessments of disturbance dynamics are still scarce. In this study, we investigated Chinese fir (Cunninghamia lanceolata), Armand pine (Pinus armandii), and Yunnan pine (Pinus yunnanensis) plantations in the mountainous eastern Yunnan Plateau. We developed a Spatial Coupling Framework of Disturbance Legacy (SC-DL) to systematically elucidate the spatial associations between contemporary species distribution patterns and historical disturbance regimes. Using the Google Earth Engine (GEE) platform, we reconstructed pixel-level disturbance trajectories by integrating long-term Landsat time series (1993–2024) and applying the LandTrendr algorithm. By fusing multi-source remote sensing features (Sentinel-1/2) with terrain factors, employing RFE, and performing a multi-model comparison, we generated 10 m-resolution species distribution maps for 2024. Spatial overlay analysis quantified the cumulative proportion of the historically disturbed area and the spatial aggregation patterns of historical disturbances within current species ranges. Key results include the following: (1) The model predicting disturbance year achieved high accuracy (R² = 0.95, RMSE = 2.02 years, MAE = 1.15 years). The total disturbed area from 1993 to 2024 was 872.7 km², exhibiting three distinct phases. (2) The random forest (RF) model outperformed other classifiers, achieving an overall accuracy (OA) of 95.17% and a Kappa coefficient (K) of 0.93. Elevation was identified as the most discriminative feature. (3) Significant spatial differentiation in disturbance types emerged: anthropogenic disturbances (e.g., logging and reforestation/afforestation) dominated (63.1% of total disturbed area), primarily concentrated within Chinese fir zones (constituting 70.2% of disturbances within this species’ range). Natural disturbances accounted for 36.9% of the total, with fire dominating within the Yunnan pine range (79.3% of natural disturbances in this zone) and drought prevailing in the Armand pine range (71.3% of natural disturbances in this zone). (4) Cumulative disturbance characteristics differed markedly among species zones: Chinese fir zones exhibited the highest cumulative proportion of disturbed area (42.6%), with strong spatial aggregation. Yunnan pine zones followed (36.5%), exhibiting disturbances linearly distributed along dry–hot valleys. Armand pine zones showed the lowest proportion (20.9%), characterized by sparse disturbances within fragmented, high-altitude habitats. These spatial patterns reflect the combined controls of topographic adaptation, management intensity, and environmental stress. Our findings establish a scientific basis for identifying disturbance-prone areas and inform the development of differentiated precision management strategies for plantations. Full article

The present study provides a comprehensive characterization of soil elemental composition in the Nile Delta, Egypt. The soil samples were analyzed using Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES), highly appropriative for the major element determination and Inductively Coupled Plasma Mass Spectrometry (ICP–MS), outstanding for the trace element analysis. A total of 55 elements were measured across 53 soil samples. A variety of statistical and analytical techniques, including both descriptive and inferential methods, were employed to assess the elemental composition of the soil. Bivariate and multivariate statistical analyses, discriminative ternary diagrams, ratio biplots, and unsupervised machine learning algorithms—such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Hierarchical Agglomerative Clustering (HAC)—were utilized to explore the geochemical similarities between elements in the soil. The application of t-SNE for soil geochemistry is still emerging and is characterized by the fact that it preserves the local distribution of elements and reveals non-linear relationships in geochemical research compared to PCA. Geochemical background levels were estimated using Bayesian inference, and the impact of outliers was analyzed. Pollution indices were subsequently calculated to assess potential contamination. The findings suggest that the studied areas do not exhibit significant pollution. Variations in background levels were primarily attributed to the presence of outliers. The clustering results from PCA and t-SNE were consistent in terms of accuracy and the number of identified groups. Four distinct groups were identified, with soil samples in each group sharing similar geochemical properties. While PCA is effective for linear data, t-SNE proved more suitable for nonlinear dimensionality reduction. These results provide valuable baseline data for future research on the studied areas and for evaluating their environmental situation. Full article

Artificial afforestation plays a critical role in ecological restoration, but its implementation involves multiple strategies—such as new afforestation, densification, and replacement afforestation. Long-term spatial and temporal identification of these tree planting program scenarios (TPPSs) is key to evaluating ecological restoration policies, yet existing pixel-based time series change detection methods still face challenges in discriminating these patterns on a large scale. To address these challenges, we propose CFDC, the first framework that synergistically integrates Continuous Change Detection (CCD) for temporal spectral trajectories and Focal Context (FC) analysis for spatial neighborhood context. A Spatiotemporal Coupling Index (STCI) is proposed to abstractly summarize the two modules, and a rule-based model classifies TPPSs by their unique temporal–spatial signatures. Implemented on Google Earth Engine (GEE) for Bayi District, Tibet, CFDC delivered overall accuracies of 76.0–82.5% from 2007 to 2022, with user’s accuracies for all TPPS types exceeding 75% in most years. Detected TPPS timelines coincide with documented ecological restoration projects within a ±1-year tolerance. Overall, CFDC offers a novel mechanism that fuses spatiotemporal features to effectively distinguish new afforestation, densification, and replacement afforestation scenarios, addressing the limitations of previous methods and enabling more accurate and scalable TPPS monitoring, thereby supporting scalable artificial forest management and ecological restoration planning. Full article

Mollusc shells serve as valuable biogeochemical archives of natural or anthropogenic processes occurring in the aquatic environment throughout the life of the molluscs. One such process is trace element pollution, which can be assessed by analyzing the elemental composition of mollusc shells. However, different mollusc species accumulate elements in their shells from the aquatic environment at varying concentrations, and specific patterns of this accumulation remain largely unknown. In the present study, we measured the concentrations of 33 elements in the shells of five commercially important Black Sea molluscs, all collected from the same site, using inductively coupled plasma mass spectrometry. The species were ranked according to the number of elements with the highest concentrations in their shells as follows: Crassostrea gigas (9) = Rapana venosa (9) = Anadara kagoshimensis (9) > Flexopecten glaber ponticus (4) > Mytilus galloprovincialis (2). Cluster analysis of Pearson’s coefficients of correlation of elemental concentrations in the molluscan shells revealed significant separation of C. gigas, F. glaber ponticus, and M. galloprovincialis. Multivariate ordination analyses allowed the accurate classification of >92.3% of shell samples using as few as four elements (Fe, As, Sr, and I). Linear discriminant analysis revealed the probability of separation of all species based on the concentrations of these elements in their shells being not lower than 79%. The applied multivariate approach based on the analysis of four base elements in shells can help not only in the taxonomic identification of molluscs, but also, upon appropriate calibration, in monitoring medium-term dynamics of trace elements in the aquatic environment. Full article

This study investigates a non-destructive, compact pulse-echo ultrasonic method that combines an external transmitter with a single receiving sensor to identify different surface treatments applied to cementitious materials. The primary objective was to evaluate whether treatment-induced acoustic changes could be reliably quantified using time-domain signal parameters. Three types of surface conditions were examined: untreated reference specimens (R), specimens treated with a standard lithium silicate solution (A), and those treated with an enriched formulation containing hexylene glycol (B) intended to enhance pore sealing via gelation. A broadband piezoelectric receiver collected the backscattered echoes, from which the maximum amplitude, root mean square (RMS) voltage, signal energy, and effective duration were extracted. Receiver operating characteristic (ROC) analysis was conducted to quantify the discriminative power of each parameter. The results showed excellent classification performance between groups involving the B-treatment (AUC ≥ 0.96), whereas the R vs. A comparison yielded moderate separation (AUC ≈ 0.61). Optimal cut-off values were established using the Youden index, with sensitivity and specificity exceeding 96% in the best-performing scenarios. The results demonstrate that a single-receiver, one-sided pulse-echo arrangement coupled with straightforward amplitude metrics provides a rapid, cost-effective, and field-adaptable tool for the quality control of silicate-surface treatments. By translating laboratory ultrasonics into a practical on-site protocol, this study helps close the gap between the experimental characterisation and real-world implementation of surface-treatment verification. Full article

As a core task in remote sensing image processing, change detection plays a vital role in dynamic surface monitoring for environmental management, urban planning, and agricultural supervision. However, existing methods often suffer from missed detection of small targets and pseudo-change interference, stemming from insufficient modeling of multi-scale feature coupling and spatio-temporal differences due to factors such as background complexity and appearance variations. To this end, we propose a Differential-Perception-Enhanced Multi-Scale Attention Network for Remote Sensing Image Change Detection (MDNet), an optimized framework integrating multi-scale feature extraction, cross-scale aggregation, difference enhancement, and context modeling. Through the parallel collaborative mechanism of the designed Multi-Scale Feature Extraction Module (EMF) and Cross-Scale Adjacent Semantic Information Aggregation Module (CASAM), multi-scale semantic learning is strengthened, enabling fine-grained modeling of change targets of different sizes and improving small-target-detection capability. Meanwhile, the Differential-Perception-Enhanced Module (DPEM) and Transformer structure are introduced for global–local coupled modeling of spatio-temporal differences. They enhance spectral–structural differences to form discriminative features, use self-attention to capture long-range dependencies, and construct multi-level features from local differences to global associations, significantly suppressing pseudo-change interference. Experimental results show that, on three public datasets (LEVIR-CD, WHU-CD, and CLCD), the proposed model exhibits superior detection performance and robustness in terms of quantitative metrics and qualitative analysis compared with existing advanced methods. Full article

Water is essential to human civilization and development, yet its quality is increasingly threatened by climate change, pollution, and resource mismanagement. This work introduces an empirical, non-invasive framework for assessing water potability using electrical impedance spectroscopy (EIS) combined with a novel equivalent circuit model. A customized sensor holder was designed to reduce impedance magnitude and enhance phase sensitivity, improving detection accuracy. Various water samples, including seawater, groundwater, and commercially bottled water, were analyzed. The proposed method achieved a 100% classification accuracy in distinguishing among water types, as validated by extracted circuit parameters and verified by inductively coupled plasma (ICP) measurements. Sensitivity analysis demonstrated the ability to detect compositional changes as small as 10%, highlighting a strong potential for fine discrimination of ionic contents. The extracted parameters, such as resistance, capacitance, and inductance, showed clear correlations with ionic composition, enabling reliable potability classification in accordance with WHO guidelines. The approach is rapid, label-free, and suitable for field applications, offering a promising tool for real-time water quality monitoring and supporting sustainable water resource management. Full article

Illegal logging is a major threat to tropical forests; however, control mechanisms and efforts to combat illegal logging have not effectively curbed fraud in the production chain, highlighting the need for effective methods to verify the geographic origin of timber. This study investigates the application of multi-elemental analysis combined with Principal Component Analysis (PCA) to discriminate the provenance of tropical timber in the Brazilian Legal Amazon. Wood samples of Hymenaea courbaril L. (Jatobá), Handroanthus sp. (Ipê), and Manilkara huberi (Ducke) A. Chevalier. (Maçaranduba) were taken from multiple sites. Elemental concentrations were determined via Inductively Coupled Plasma Mass Spectrometry (ICP-MS), and CA was applied to evaluate geographic differentiation. Significant differences in elemental profiles were found among locations, particularly when using the intermediate disk portions (25% to 75%), and especially the average of all five sampled portions, which proved most effective in geographic discrimination of the trunk. Elements such as Ca, Sr, Cr, Cu, Zn, and B were especially important for spatial discrimination. These findings underscore the forensic potential of multi-elemental wood profiling as a tool to support law enforcement and environmental monitoring by providing scientifically grounded evidence of timber origin. Full article

Breast cancer detection through non-invasive and accurate techniques remains a critical challenge in medical diagnostics. This study introduces a deep learning-based framework that leverages a microwave radar system equipped with an arc-shaped array of six antennas to estimate key tumor parameters, including position, size, and depth. This research begins with the evolutionary design of an ultra-wideband octagram ring patch antenna optimized for enhanced tumor detection sensitivity in directional near-field coupling scenarios. The antenna is fabricated and experimentally evaluated, with its performance validated through S-parameter measurements, far-field radiation characterization, and efficiency analysis to ensure effective signal propagation and interaction with breast tissue. Specific Absorption Rate (SAR) distributions within breast tissues are comprehensively assessed, and power adjustment strategies are implemented to comply with electromagnetic exposure safety limits. The dataset for the deep learning model comprises simulated self and mutual S-parameters capturing tumor-induced variations over a broad frequency spectrum. A core innovation of this work is the development of the Attention-Based Feature Separation (ABFS) model, which dynamically identifies optimal frequency sub-bands and disentangles discriminative features tailored to each tumor parameter. A multi-branch neural network processes these features to achieve precise tumor localization and size estimation. Compared to conventional attention mechanisms, the proposed ABFS architecture demonstrates superior prediction accuracy and interpretability. The proposed approach achieves high estimation accuracy and computational efficiency in simulation studies, underscoring the promise of integrating deep learning with conformal microwave imaging for safe, effective, and non-invasive breast cancer detection. Full article