Search Results (19,840)

Listeria monocytogenes is a foodborne pathogen capable of contaminating ready-to-eat meat products, e.g., frankfurters. Post-packaging mild heat treatment via water immersion is commonly employed; however, this may be sublethal to cells located in protected niches or beneath the product surface. The objectives of this study were to evaluate thermal injury of L. monocytogenes on frankfurters at single-cell versus population level and to comparatively estimate pathogens’ physiological status. Pork frankfurter-type sausages were inoculated (ca. 7.0‒7.5 log CFU/cm²) with L. monocytogenes strain EGDE-e. Heat treatment was performed at 61°C (max. 60 min) and 64 °C (max. 12 min). To determine the injured subpopulation from the total, tryptic soy agar with 0.6% yeast extract (TSAYE), supplemented or not with 5% NaCl, was used. Plating-based quantification of injured cells was compared to CFDA/PIstained cells analysed by fluorescence microscopy and quantified with Fiji software. Injury was recorded mainly after 2 and 4 min at 64 °C, whereas no injury was detected at 61 °C, at population level. Following exposure to 61 °C for 60 min, culturable cells dropped below the enumeration limit (0.3 log CFU/cm²), while a considerable number of CFDA⁺/PI⁻ and CFDA⁺/PI⁺ cells indicated viable-but-non-culturable induction and sublethal injury, respectively. These findings suggest that non-culturability may limit the accuracy of solely culture-based enumeration methods. Full article

Meteorological and climatological trends are surely changing the way urban infrastructure systems need to be operated and maintained. Urban road traffic fluctuates more significantly under the interference of strong wind–rain weather, especially during tropical cyclones. Deep learning-based methods have significantly improved the accuracy of traffic prediction under extreme weather, but their robustness still has much room for improvement. As the frequency of extreme weather events increases due to climate change, accurately predicting spatiotemporal patterns of urban road traffic is crucial for a resilient transportation system. The compounding effects of the hazards, environments, and urban road network determine the spatiotemporal distribution of urban road traffic during an extreme weather event. In this paper, a novel Knowledge-driven Attribute-Augmented Attention Spatiotemporal Graph Convolutional Network (KA3STGCN) framework is proposed to predict urban road traffic under compound hazards. We design a disaster-knowledge attribute-augmented unit to enhance the model’s ability to perceive real-time hazard intensity and road vulnerability. The attribute-augmented unit includes the dynamic hazard attributes and static environment attributes besides the road traffic information. In addition, we improve feature extraction by combining Graph Convolutional Network, Gated Recurrent Unit, and the attention mechanism. A real-world dataset in Shenzhen City, China, was employed to validate the proposed framework. The findings show that the prediction accuracy of traffic speed can be significantly increased by 12.16%~31.67% with disaster information supplemented, and the framework performs robustly on different road vulnerabilities and hazard intensities. The framework can be migrated to other regions and disaster scenarios in order to strengthen city resilience. Full article

Background: Malnutrition and systemic inflammation are major determinants of poor outcomes in hospitalized older adults, such as length of hospital stay (LOS), mortality, and readmission risk. The C-reactive protein to albumin ratio (CRP/Alb) and the Prognostic Nutritional Index (PNI) are simple biomarkers reflecting inflammation and nutritional status. Additionally, the PNI offers a straightforward method to assess both the nutritional state and mortality risk in older patients. Objective: The objective of this study was to compare the predictive accuracy of the CRP/Alb ratio and PNI for early in-hospital mortality at 7 and 30 days after admission in older patients, independent of admitting diagnosis. Methods: We retrospectively analyzed 2776 patients aged 65 years and older, admitted to the Internal Medicine and Aging Unit of the “Policlinico Riuniti” University Hospital in Foggia, Italy, between 2019 and 2025. Laboratory data at admission included CRP, albumin, and total lymphocyte count (TLC). The CRP/Alb ratio and PNI were calculated. Prognostic performance for 7- and 30-day mortality for both the CRP/Alb ratio and PNI was assessed using ROC curves, Cox regression, Kaplan–Meier survival analyses, and positive predictive value (PPV) comparisons, stratified by rehospitalization status and length of stay (LOS). The likelihood-ratio test was also performed to compare the 7- and 30-day mortality PPVs of the CRP/Alb ratio and the PNI, both for all patients and for re-hospitalized patients. Results: In-hospital mortality occurred in 444 patients (16%). Deceased patients showed significantly higher CRP/Alb ratios and lower PNI values than survivors (p < 0.001). Both the CRP/Alb ratio and PNI independently predicted 7- and 30-day mortality. A CRP/Alb ratio >8 strongly predicted very early mortality (HR 10.46 for 7-day death), whereas a PNI <38 predicted both 7- and 30-day mortality (HR 8.84 and HR 3.54, respectively). Among non-rehospitalized patients, the PNI demonstrated superior predictive performance regardless of LOS (p < 0.001). Among rehospitalized patients, the PNI was a more accurate predictor for short LOS (<7 days), while the CRP/Alb ratio performed better for longer LOS (≥7 days). Conclusions: Both the CRP/Alb ratio and PNI are inexpensive, readily available biomarkers for early risk stratification in hospitalized older adults. The CRP/Alb ratio is particularly effective in detecting very early mortality risk, while the PNI offers refined prognostic value across selected subgroups. Integrating these markers at admission may support personalized geriatric care and timely interventions. Full article

The article presents a mathematical description of the thermal phenomena occurring both inside and on the surfaces of a homogeneous plate subjected to an external heat flux on one side. Analytical formulae for thermal excitation, with a given duration and constant power, are derived, enabling the determination of temperature increases on both the heated and unheated surfaces of the plate under specific heat transfer conditions to the surroundings. Convective heat transfer, with individual heat transfer coefficients on both sides of the slab, is considered; however, radiative heat loss can also be included. The solution of the problem obtained using two methods is presented: the method of separation of variables (MSV) and the Laplace transform (LT). The advantages and disadvantages of both analytical formulae, as well as the impact of various factors on the accuracy of the solution, are discussed. Among others, the MSV solution works well for a sufficiently long time, whereas the LT solution is better for a sufficiently short time. The theoretical considerations are illustrated with diagrams for several configurations, each representing various heat transfer conditions on both sides of the plate. The presented solution can serve as a starting point for further analysis of more complex geometries or multilayered structures, e.g., in non-destructive testing using active thermography. The developed theoretical model is verified for a determination of the thermal diffusivity of a reference material. The model can be useful for analyzing the method’s sensitivity to various factors occurring during the measurement process, or the method can be adapted to a pulse of known duration and constant power, which is much easier to implement technically than a very short impulse (Dirac) with high energy. Full article

Accurate soil organic carbon (SOC) estimation is critical for assessing ecosystem services, carbon budgets, and informing sustainable land management, particularly in ecologically sensitive mountainous regions. This study focuses on modelling the spatial distribution of SOC within the heterogeneous volcanic landscape of the Nevado de Toluca (NdT), central Mexico, an area spanning 535.9 km² and characterised by diverse land uses, altitudinal gradients, and climatic regimes. Using 29 machine learning algorithms, we evaluated the predictive capacity of three key variables: land use, elevation, and the Normalised Difference Vegetation Index (NDVI) derived from satellite imagery. Complementary analyses were performed using the Bare Soil Index (BSI) and the Modified Soil-Adjusted Vegetation Index 2 (MSAVI2) to assess their relative performance. Among the tested models, the Quadratic Support Vector Machine (SVM) using NDVI, elevation, and land use emerged as the top-performing model, achieving a coefficient of determination (R²) of 0.84, indicating excellent predictive accuracy. Notably, 14 models surpassed the R² threshold of 0.80 when using NDVI and BSI as predictor variables, whereas MSAVI2-based models consistently underperformed (R² < 0.78). Validation plots demonstrated strong agreement between observed and predicted SOC values, confirming the robustness of the best-performing models. This research highlights the effectiveness of integrating multispectral remote sensing indices with advanced machine learning frameworks for SOC estimation in mountainous volcanic ecosystems Full article

This study presents a new method for estimating the seismic responses of multi-story structures equipped with a cost-effective earthquake protection system. This system comprises a graphite lubrication interface, targeting a friction coefficient of approximately 0.2, and a feasible restoring force mechanism to suppress residual displacements. It utilizes the concept of sliding systems through conventional and affordable construction materials although it acts like a fixed-based structure until exceeding the threshold level. This multi-story estimation procedure is an extension of the recently developed procedure for estimating the shear coefficient of a single-story sliding structure with a restoring force mechanism. In the new estimation procedure, a multi-story superstructure is firstly regarded as a single-story superstructure to determine the shear coefficient. Then, the shear coefficient is distributed to each story through floor distribution coefficients considering the mass ratios. The contribution of ground motion intensity is also incorporated into the new form for improving accuracy. For this examination, incremental dynamic analyses (IDAs) are performed for three and six-story free-standing structures, both with and without a restoring force capability. The results clarify the reliability of the new estimation, which matched the IDA results within the ±20% error. The improvement in accuracy achieved by incorporating ground motion intensity is also clarified. The multi-story estimation with the improvement can reasonably estimate the seismic response of sliding structures, without dynamic analysis, solely based on structural properties. This greatly benefits the design process. Furthermore, the IDA results clarified the significant benefits of multi-story sliding structures employing graphite lubrication and properly designed restoring force mechanisms in reducing structural damage and suppressing residual sliding displacements. Full article

Monitoring soil heavy metal contamination in urban land to protect human health requires rapid and low-cost methods. Visible and infrared (vis-NIR) spectroscopy shows strong promise for monitoring metals such as copper (Cu). However, an important question is how “spectrally nearby” samples influence Cu estimation models. This study investigates that issue in depth. We collected 250 soil samples from Shenzhen City, China (the world’s tenth-largest city). During building the model, we selected spectrally nearby samples for each validation sample, varying the number of neighbors from 20 to 200 by adding one sample at a time. Results show that, compared with the traditional method, incorporating nearby samples substantially improved Cu prediction: the coefficient of determination in prediction (${\mathrm{R}}_{\mathrm{p}}^2$) increased from 0.75 to 0.92, and the root mean square error of prediction (RMSEP) decreased from 8.56 to 4.50 mg·kg⁻¹. The optimal number of nearby samples was 125, representing 62.25% of the dataset. And the performance followed an L-shape curve as the number of neighbors increased—rapid improvement at first, then stabilization. We conclude that using spectrally nearby samples is an effective way to improve vis-NIR Cu estimation models. The optimal number of neighbors should balance model accuracy, robustness, and complexity. Full article

In today’s digital economy, electronic payments are essential to supporting financial transactions. However, the danger of fraud also rises with company complexity and volume. This study uses machine learning and advanced analytics to investigate fraud detection in electronic payments. Using business tools like accounts, account types, and balance sheets, we spot patterns and trends connected to illicit activities. To detect and identify fraud, our study uses pre-existing data, machine learning algorithms, and infrastructure. The author has assessed the performance of several models, such as logistic regression, random forests, and k-nearest neighbor models, using criteria like accuracy, precision, and recall. To determine the most important characteristics for fraud detection, the author also conducts a significance analysis and examines the model’s interpretability. According to the current study’s findings, financial institutions and payment systems will be able to identify fraud more efficiently and gain an improved knowledge of the traits of commercial fraud. Full article

New technologies and equipment for medicine analysis and diagnostics have always been critical in clinical medication and pharmaceutical production. Especially in the field of traditional Chinese medicine (TCM) where the chemical composition is not fully clear, cross-device analysis and identification using the same technology can sometimes even lead to misjudgments. Akebia species, capable of inducing heat clearing, diuresis, and anti-inflammatory effects, show great potential in clinical applications. However, the three commonly used species differ in pharmacological effects and therefore should not be used interchangeably. We proposed a method combining LIBS with random forest for species identification and established a modeling and verification scheme across device platforms. Spectra of three Akebia species were collected using two LIBS systems equipped with spectrometers of different resolutions. The data acquired from the low-resolution spectrometer were used for model training, while the data from the high-resolution spectrometers were used for testing. A spectral correction and feature selection (SCFS) method was proposed, in which spectral data were first corrected using a standard lamp, followed by feature selection via analysis of variance (ANOVA) to determine the optimal number of discriminative features. The highest classification accuracy of 80.61% was achieved when 28 features were used. Finally, a post-processing (PP) strategy was applied, where abnormal spectra in the test set were removed using density-based spatial clustering of applications with noise (DBSCAN), resulting in a final classification accuracy of 85.50%. These results demonstrate that the proposed “SCFS-PP” framework effectively enhances the reliability of cross-instrument data utilization and expands the applicability of LIBS in the field of TCM. Full article

The contamination of food and beverages with heavy metals, such as Cd, presents significant health risks, underscoring the need for reliable and sensitive analytical methods. This study introduces the development of a rapid, cost-effective, and environmentally friendly method for Cd determination in cachaça, a traditional Brazilian sugarcane spirit. Magnetic nanoparticles (Fe₃O₄) functionalized with tetraethyl orthosilicate are synthesized and employed as adsorbents in a dispersive magnetic solid-phase extraction procedure. The extracted Cd is quantified using flame atomic absorption spectrometry. A full factorial experimental design is used to optimize key parameters, including the sorbent mass, adsorption time, desorption time, and acid concentration. The method demonstrates excellent analytical performance, with a linear calibration range (R² = 0.99), detection limit of 0.0046 mg L⁻¹, and quantification limit of 0.0200 mg L⁻¹. Moreover, validation results show high precision (coefficient of variation < 9.10%) and accuracy (recovery rates between 92.00% and 120.00%). When analyzing commercial cachaça samples, cadmium was detected in all five specimens. Notably, in one sample the cadmium concentration exceeded Brazil’s maximum permissible limit of 0.0200 mg kg⁻¹, underscoring the importance of this work for ensuring food safety. The proposed method offers a sensitive, reproducible, and sustainable approach for analysis of potentially toxic trace metals in alcoholic beverages, reinforcing its potential for routine monitoring and regulatory compliance. Full article

With the continuous acceleration of industrialization, air pollution has become increasingly severe and has, to some extent, contributed to the progression of global climate change. Against this backdrop, accurate temperature forecasting plays a vital role in various fields, including agricultural production, energy scheduling, environmental governance, and public health protection. To improve the accuracy and stability of temperature prediction, this study proposes a hybrid modeling approach that integrates convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, and random forests (RFs). This model fully leverages the strengths of CNNs in extracting local spatial features, the advantages of LSTM in modeling long-term dependencies in time series, and the capabilities of RF in nonlinear modeling and feature selection through ensemble learning. Based on daily temperature, meteorological, and air pollutant observation data from Wuhan during the period 2015–2023, this study conducted multi-scale modeling and seasonal performance evaluations. Pearson correlation analysis and random forest-based feature importance ranking were used to identify two key pollutants (PM_2.5 and O₃) and two critical meteorological variables (air pressure and visibility) that are strongly associated with temperature variation. A CNN-LSTM model was then constructed using the meteorological variables as input to generate preliminary predictions. These predictions were subsequently combined with the concentrations of the selected pollutants to form a new feature set, which was input into the RF model for secondary regression, thereby enhancing the overall model performance. The main findings are as follows: (1) The six major pollutants exhibit clear seasonal distribution patterns, with generally higher concentrations in winter and lower in summer, while O₃ shows the opposite trend. Moreover, the influence of pollutants on temperature demonstrates significant seasonal heterogeneity. (2) The CNN-LSTM-RF hybrid model shows excellent performance in temperature prediction tasks. The predicted values align closely with observed data in the test set, with a low prediction error (RMSE = 0.88, MAE = 0.66) and a high coefficient of determination (R² = 0.99), confirming the model’s accuracy and robustness. (3) In multi-scale forecasting, the model performs well on both daily (short-term) and monthly (mid- to long-term) scales. While daily-scale predictions exhibit higher precision, monthly-scale forecasts effectively capture long-term trends. A paired-sample t-test on annual mean temperature predictions across the two time scales revealed a statistically significant difference at the 95% confidence level (t = −3.5299, p = 0.0242), indicating that time granularity has a notable impact on prediction outcomes and should be carefully selected and optimized based on practical application needs. (4) One-way ANOVA and the non-parametric Kruskal–Wallis test were employed to assess the statistical significance of seasonal differences in daily absolute prediction errors. Results showed significant variation across seasons (ANOVA: F = 2.94, p = 0.032; Kruskal–Wallis: H = 8.82, p = 0.031; both p < 0.05), suggesting that seasonal changes considerably affect the model’s predictive performance. Specifically, the model exhibited the highest RMSE and MAE in spring, indicating poorer fit, whereas performance was best in autumn, with the highest R² value, suggesting a stronger fitting capability. Full article

This paper presents an analysis of the dynamic properties of the Rogowski coil, primarily by determining the dynamic errors for several selected test signals and the upper bound of the dynamic error for two quality criteria: the integral-square error and the absolute error. A procedure for filtering and reproducing these signals is also presented. The foundation of the presented research is an equivalent circuit model of the Rogowski coil, developed primarily for applications in electrical power and electromechanical systems. Two novel aspects of this work are the determination of dynamic errors for the Rogowski coil and a graphical and quantitative comparison of their values. The research results presented in this paper may serve as a foundation for enhancing the accuracy and dynamic reliability of both the Rogowski coil and other devices (e.g., transformers and current transformers) used in the power industry and mechanical engineering, particularly in the condition monitoring of a broad range of power equipment and in the experimental analysis of electromechanical systems operating under variable load conditions. The findings also highlight the importance of accurate current measurement in modern energy systems, where transient and high-frequency components increasingly affect performance and reliability. Consequently, the presented methodology provides a useful framework for guiding sensor selection and signal processing strategies in advanced monitoring and control applications. Full article

The environmental noise impact on sensitive buildings and residents, generated by urban rail transit systems, has attracted increasing attention from the public and various levels of management. Owing to the diversity of building types and the complexity of noise propagation paths, the accurate prediction of noise levels adjacent to structures through traditional experimental or empirical formula-based methods is challenging. In this paper, on-site multi-dimensional noise monitoring of the noise source affecting the sensitive buildings was first carried out, and a hybrid prediction method combining normative formulas, numerical simulations, and experimental research is proposed and validated. This approach effectively addresses the shortcomings of traditional prediction methods in terms of source strength determination, propagation path distribution, and accuracy of results. The results show that, while predicting or assessing the noise impact on sensitive buildings and interior residents, it is important to properly consider the impact of background noise (such as road traffic) as well as vibration radiation noise of bridge structures. The predicted results obtained by using this method closely match the measured results, with errors controlled within 3 dB(A). The noise prediction error in front of buildings is controlled within 2 dB(A), fully meeting the requirements for environmental noise assessment. Full article

The shoreline and seabed topography are key components of the coastal zone and are essential for hydrographic surveys, shoreline process modelling, and coastal infrastructure management. The development of unmanned aerial vehicles (UAVs) and optoelectronic sensors, such as photogrammetric cameras and airborne laser scanning (ALS) using light detection and ranging (LiDAR) technology, has enabled the acquisition of high-resolution bathymetric data with greater accuracy and efficiency than traditional methods using echo sounders on manned vessels. This article presents a spatial analysis of bathymetric data obtained from UAV photogrammetry and ALS LiDAR, focusing on shallow-water depth estimation and shoreline extraction. The study area is Lake Kłodno, an inland waterbody with moderate ecological status. Aerial imagery from the photogrammetric camera was used to model the lake bottom in shallow areas, while the LiDAR point cloud acquired through ALS was used to determine the shoreline. Spatial analysis of support vector regression (SVR)-based bathymetric data showed effective depth estimation down to 1 m, with a reported standard deviation of 0.11 m and accuracy of 0.22 m at the 95% confidence, as reported in previous studies. However, only 44.5% of 1 × 1 m grid cells met the minimum point density threshold recommended by the National Oceanic and Atmospheric Administration (NOAA) (≥5 pts/m²), while 43.7% contained no data. In contrast, ALS LiDAR provided higher and more consistent shoreline coverage, with an average density of 63.26 pts/m², despite 27.6% of grid cells being empty. The modified shoreline extraction method applied to the ALS data achieved a mean positional accuracy of 1.24 m and 3.36 m at the 95% confidence level. The results show that UAV photogrammetry and ALS laser scanning possess distinct yet complementary strengths, making their combined use beneficial for producing more accurate and reliable maps of shallow waters and shorelines. Full article

Time-Weighted Dynamic Time Warping (TWDTW), adapted from speech recognition, is used in agricultural remote sensing to model crop growth, particularly under limited ground sample conditions. However, most related studies rely on full-season or empirically selected features, overlooking the systematic optimization of features at each observation time to improve TWDTW’s performance. This often introduces a large amount of redundant information that is irrelevant to crop discrimination and increases computational complexity. Therefore, this study focused on maize as the target crop and systematically conducted mapping experiments using Sentinel-1/2 images to evaluate the potential of integrating TWDTW with optimally selected multi-source time series features. The optimal multi-source time series features for distinguishing maize from non-maize were determined using a two-step Jeffries Matusita (JM) distance-based global search strategy (i.e., twelve spectral bands, Normalized Difference Vegetation Index, Enhanced Vegetation Index, and the two microwave backscatter coefficients collected during the maize jointing to tasseling stages). Then, based on the full-season and optimal multi-source time series features, we compared TWDTW with two widely used temporal machine learning models in agricultural remote sensing community. The results showed that TWDTW outperformed traditional supervised temporal machine learning models. In particular, compared with TWDTW driven by the full-season optimal multi-source features, TWDTW using the optimal multi-source time series features improved user accuracy by 0.43% and 2.30%, and producer accuracy by 7.51% and 2.99% for the years 2020 and 2021, respectively. Additionally, it reduced computational costs to only 25% of those driven by the full-season scheme. Finally, maize maps of Yangling District from 2020 to 2023 were produced by optimal multi-source time series features-based TWDTW. Their overall accuracies remained consistently above 90% across the four years, and the average relative error between the maize area extracted from remote sensing images and that reported in the statistical yearbook was only 6.61%. This study provided guidance for improving the performance of TWDTW in large-scale crop mapping tasks, which is particularly important under conditions of limited sample availability. Full article