Search Results (626)

This study reviews and experimentally investigates critical logistics and transport models in stainless-steel (SS) fluid storage tanks, focusing on stainless steel grades 316 and 304L. Conceptual vessel schematics emphasize hygienic drainability, refill uniformity, and thermal control, supported by representative 316L properties for heat-transfer, stress, and fluid–structure analyses. At the logistics scale, modelling integrates component-level simulations, computational fluid dynamics (CFD), and Finite Element Method (FEM) with network-level approaches, such as Continuous Approximation, to address facility location, refilling schedules, and demand variability. Experimental characterization using EDS and XRF confirmed the expected Cr/Ni backbone and grade-consistent Mo in 316, while unexpected C, Mn, and Cu readings were attributed to instrumental limits or statistical variance. Unexpected detection of Europium in 304L highlights the need for further mechanical testing. Overall, combining simulation, logistics modelling, and compositional verification offers a coherent framework for safe, efficient, and thermally reliable stainless-steel tank design. Full article

Background and Objectives: Avian influenza poses a continuous public health threat, particularly to individuals with occupational exposure to poultry such as farm workers, live animal market employees, and processing plant staff. This study aimed to investigate the systemic metabolic effects of such exposure and to identify potential biomarkers for early detection and health risk assessment. Materials and Methods: An untargeted liquid chromatography–mass spectrometry (LC-MS)-based metabolomics approach was applied to analyze serum samples from occupationally exposed individuals and healthy controls. Multivariate statistical analysis, pathway enrichment, and topology analysis were performed to identify significantly altered metabolites and metabolic pathways. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to select key metabolites. Results: Multivariate statistical analysis revealed a clear separation between the exposure group and control, suggesting distinct metabolic profiles between the two populations. Pathway analysis indicated significant alterations in alanine, aspartate, and glutamate metabolism, as well as tryptophan metabolism, which are closely linked to immune regulation, energy metabolism, and host–pathogen interactions. LASSO feature selection and subsequent manual verification identified 17 key metabolites with strong discriminative power. Furthermore, lipidomic profiling revealed a pronounced increase in lysophosphatidylcholine (LPC) levels and a concurrent decrease in phosphatidylcholine (PC) species in exposed individuals. Conclusions: This study reveals metabolic disruptions associated with occupational avian influenza exposure and identifies potential serum biomarkers related to immune and lipid metabolism. These findings provide novel insights into host responses to avian influenza exposure and may support early detection and health risk assessment in high-risk occupational populations. Full article

The accurate calibration of radiochromic films is critical for high dose rate (HDR) brachytherapy dosimetry. Conventional workflows frequently rely on manually determined regions of interest (ROIs), which might increase operator variability. In this investigation, Gafchromic EBT3 films were irradiated under clinical settings at nominal doses of 0–10 Gy and evaluated using a MATLAB (R2024b)-based tool that allows for both manual and automated ROI selection. The calibration curves were modeled with a second-order polynomial and rational model, and performance was assessed using statistical measures. The study found that the rational model fits better than the polynomial model. Additionally, the automatic ROI approach outperformed the manual method in both models, resulting in higher calibration accuracy and reproducibility (R² = 0.999, RMSE = 0.118 Gy, MAE = 0.103 Gy vs. R² = 0.986, RMSE = 0.448 Gy, MAE = 0.388 Gy). Although manual ROI occasionally produced greater dose–response slopes at higher doses, it was more susceptible to operator bias and film non-uniformity. In contrast, automatic ROI reduced variability by consistently picking homogeneous sections, resulting in steady curve fitting across the entire dose range. Furthermore, a companion module transformed calibrated films into 2D false-color maps and 3D dosage surfaces, allowing for visual assessment of dose uniformity, detection of scanner-related aberrations, and quantitative verification for quality assurance. These findings demonstrate that automated ROI selection provides a more stable and reproducible foundation for film calibration in HDR brachytherapy, minimizing operator dependency while facilitating routine clinical quality assurance. Full article

Dynamic pressure measurement is an important component in the turbo engine testing process. This paper presents a comparative analysis between two types of multichannel electronic pressure measurement systems, commonly known as pressure scanners, used for this purpose: ZOC17/8Px, with analog amplification per channel, and MPS4264, a modern digital system with integrated A/D conversion. The study was conducted in two stages: a metrological verification and validation in static mode, using a high-precision pressure standard, and an experimental stage in dynamic mode, where data was acquired from a turbojet engine test stand, in constant engine speed mode. The signal stability of the pressure scanners was statistically analyzed by determining the coefficient of variation in the signal and the frequency spectrum (FFT) for each channel of the pressure scanners. Furthermore, comprehensive uncertainty budgets were calculated for both systems. The results highlight the superior stability and reduced uncertainty of the MPS4264 pressure scanner, attributing its enhanced performance to digital integration and a higher resilience to external noise. The findings support the adoption of modern digital systems for dynamic applications and provide a robust metrological basis for the optimal selection of measurement systems. Full article

As an emerging distributed energy system, microgrid power flow prediction plays a crucial role in optimizing energy dispatch and power grid operation. Traditional methods of power flow prediction mainly rely on statistics and time series models, neglecting the spatial relationships among different nodes within the microgrid. To overcome this limitation, a Physical-Informed Graph Attention Network (PI-GAT) is proposed to capture the spatial structure of the microgrid, while an attention mechanism is introduced to measure the importance weights between nodes. In this study, we constructed a representative 14-node microgrid power flow dataset. After collecting the data, we preprocessed and transformed it into a suitable format for graph neural networks. Next, an autoencoder was employed for pre-training, enabling unsupervised learning-based dimensionality reduction to enhance the expressive power of the data. Subsequently, the extended data is fed into a graph convolution module with attention mechanism, allowing adaptive weight learning and capturing relationships between nodes. And integrate the physical state equation into the loss function to achieve high-precision power flow prediction. Finally, simulation verification was conducted, comparing the PI-GAT method with traditional approaches. The results indicate that the proposed model outperforms the other latest model across various evaluation indicators. Specifically, it has 46.9% improvement in MSE and 14.08% improvement in MAE. Full article

Background and Aim: Food hygiene is fundamental to public health, ensuring safe and nutritious food free from contaminants, and is vital for economic development and sustainability. The Hazard Analysis and Critical Control Points (HACCP) system is a crucial tool for managing risks in food production. Despite global recognition of food safety’s importance, significant disparities exist, especially in Southern Italy, where diverse food production, tourism, and economic factors pose challenges to enforcing hygiene standards. This study evaluates non-compliance with food hygiene regulations within a Local Health Authority (LHA) in Calabria, Southern Italy, to inform effective public health strategies. Materials and Methods Authorized by the Food Hygiene and Nutrition Service (FHNS) of the LHA, the study covers January 2022 to December 2024, analyzing 579 enterprises with 1469 production activities. Inspections followed EC Regulation No. 852/2004, verifying the correct application of procedures based on the Hazard Analysis and Critical Control Points (HACCP) principles, including the operator’s monitoring of Critical Control Points (CCPs), and adherence to Good Hygiene Practices (GHPs). Non-compliances were classified by severity, and corrective and punitive actions were applied. Data were analyzed annually and across the full period using descriptive statistics and chi-squared tests to assess trends. Results: Inspection coverage increased markedly from 29.8% of production activities in 2022 to 62.5% in 2023, sustaining 62.0% in early 2024, exceeding the growth of new activities. Inspections were mainly triggered by RASFF alerts (22.4%), routine controls (20.0%), and verification of previous prescriptions (14.3%). The most frequent corrective measures were long-term prescriptions (28.6%), violation reports (22.9%), and short-term prescriptions (20.0%). Enterprises averaged 4.61 production activities, highlighting operational complexity. Conclusions: This study provides a granular analysis of food hygiene non-compliance within a Local Health Authority (LHA) in Southern Italy, to inform effective public health strategies. While official control data may be publicly available in some contexts, our research offers a unique, in-depth view of inspection triggers, non-compliance patterns, and corrective measures, which is crucial for understanding specific regional challenges. The analysis reveals that the prevalence of long-term prescriptions and reliance on RASFF alerts indicate systemic challenges requiring sustained interventions. Full article

Climate change is intensifying droughts, heatwaves, dust storms, and rainfall variability across Eastern Europe, undermining yields and soil stability. In Ukraine, decades of underinvestment and wartime damage have led to widespread degradation of field shelterbelts, while the adoption of agroforestry remains constrained by tenure ambiguity, fragmented responsibilities, and limited access to finance. This study develops a policy-and-technology framework to restore agroforestry at scale under severe fiscal and institutional constraints. We apply a three-stage approach: (i) a national baseline (post-1991 legislation, statistics) to diagnose the biophysical and legal drivers of shelterbelt decline, including wartime damage; (ii) a comparative synthesis of international support models (governance, incentives, finance); and (iii) an assessment of transferability of digital monitoring, reporting, and verification (MRV) tools to Ukraine. We find that eliminating tenure ambiguities, introducing targeted cost sharing, and enabling access to payments for ecosystem services and voluntary carbon markets can unlock financing at scale. A digital MRV stack—Earth observation, UAV/LiDAR, IoT sensors, and AI—can verify tree establishment and survival, quantify biomass and carbon increments, and document eligibility for performance-based incentives while lowering transaction costs relative to field-only surveys. The resulting sequenced policy package provides an actionable pathway for policymakers and donors to finance, monitor, and scale shelterbelt restoration in Ukraine and in similar resource-constrained settings. Full article

This study focuses on determining the optimal fig and butyric acid concentrations for butanol production under thermophilic conditions. Waste fig is a potentially rich substrate in sugars, minerals, and vitamins, but it is insufficient for effective butanol formation when butyrate is not present in the media because butanol is produced by butyrate reduction. Therefore, butyric acid was supplemented gradually in certain concentrations to fig-containing fermentation broth. The best combination of butyric acid and fig was determined using the Box–Wilson statistical experiment design. Fig and butyric acid concentrations were set as independent variables, while butanol concentration was the objective function. When the concentrations of butyric acid and fig were near the middle of the ranges under inspection, more butanol was produced. Butanol production was the lowest as fig and butyric acid values got closer to the extremes, particularly at high concentrations. Maximum butanol of 0.32 g/L was obtained with 16 g fig/L and 1.6 g butyric acid/L. The quadratic model generated was found to be significant, and its reliability was tested with verification experiments with reproducible results. This study showed that butanol could be produced from butyrate-supplemented fig waste under thermophilic conditions with a consolidated bioprocessing approach. Full article

The assessment of fatigue life is important for the design of pavement materials because fatigue cracks are one of the most common types of failure in pavement structures. The fatigue test is commonly used to determine the fatigue life. However, there are lots of uncertainties, such as the construction environment and personal operations, during the fatigue test due to the complexity of the pavement materials. Determining the fatigue life of pavement materials under uncertainty is a challenging task. In this study, considering cement-stabilized cold recycled mixtures (CSCRMs) as an example, an uncertainty quantification (UQ) method based on PyMC3, a novel and powerful probabilistic programming package, was developed to address the uncertainty in fatigue behavior based on fatigue tests. Probabilistic programming was employed to characterize the uncertainty of fatigue life based on fatigue test data and the fatigue life formula. The uncertainty of fatigue life was quantified by determining the unknown coefficient of the fatigue life formula. Two independent datasets for the CSCRM were used to illustrate and verify the developed method. The coefficients of determination (R²) for the prediction results of fatigue life were higher than 0.96, based on the obtained formula and test data. The maximum and average errors of the coefficients determined using the fatigue equation were less than 11% and 7%, respectively. The verification demonstrates that the predicted fatigue life closely agrees with the test data, and the determined coefficients of the fatigue equation are in excellent agreement with prior findings. The developed method avoided complex statistical computations and references. The UQ can evaluate the fatigue life and its uncertainty and significantly enhance the understanding of the fatigue behavior of the CSCRM. Full article

Climate change has intensified extreme hydrological events in cold regions, threatening the stability of river ecosystems. The traditional monthly frequency method for calculating ecological flow assumes equal guarantee rates across all months, overlooking the complex nonlinear dependencies between interannual and intermonthly flows. This approach may result in flow values for certain months during low-flow years exceeding those of corresponding months in high-flow years, failing to align with actual hydrological patterns. This study integrates Copula functions with the monthly frequency method to establish an improved ecological flow calculation framework, accurately characterizing the statistical correlation between interannual and intermonthly flow variability. The Hailang River basin in Northeast China was selected as the study area. First, the SWAT model was employed to simulate natural runoff processes from 1956 to 1965. The calibration phase demonstrated excellent performance (R² = 0.84, NSE = 0.83), and the validation phase also met standards (R² = 0.82, NSE = 0.81). The improved method selected optimal Copula functions for each month through rigorous statistical tests (AIC, BIC, RMSE, and K-S test), establishing joint probability distributions for annual and monthly average flows. The results indicate that different Copula types better align with monthly hydrological seasonal characteristics: Gaussian Copula suits February, May, and July; t-Copula suits August; Clayton Copula from September to December; Gumbel Copula for January, March, April, and June. Through conditional probability relationships (P(X₀≥x₀, 90%) = 0.9), the monthly guarantee rate range determined by the improved method spans 81.83% to 90.08%, significantly outperforming the uniform 90% guarantee rate employed by traditional methods. Verification using the Tennant method confirmed that ecological flows throughout the year met “excellent” or higher standards. Ecological flows exhibited pronounced seasonal variation, ranging from 6.2 m³/s during winter to spring to 96.93 m³/s during summer to autumn, providing scientific basis for basin-scale ecological water management. This study establishes a reliable methodological framework for ecological flow management in cold-region rivers. Full article

Access to schools is crucial in determining an area’s functioning and development, especially regarding housing development. This article presents an analysis of the spatial accessibility of educational services in the city. In Poland, municipalities applied standards for the accessibility of schools in the 1980s and 1990s. In 2023, amendments to the Law on Planning and Spatial Development (The Act of 7 July 2023 amending the Act on Spatial Planning and Development and certain other Acts) reintroduced the obligation to consider the accessibility of education services in the planning documents of municipalities and established the applicable distances. This article presents a method for assessing the level of accessibility of primary schools using spatial–statistical indicators, with the city of Płock as an example. The analysis allowed us to present the spatial differentiation of the level of fulfilment and the level of fulfilment of needs according to the new planning guidelines. We deepened the analysis grounded in the 15-Min City concept to validate the findings, benchmarking the results against international frameworks and recognised good practices. Similar analysis can support local authorities of other municipalities in the spatial planning decision-making process. The authors formulated the following research questions: What criteria can be applied to evaluate the performance of existing educational facilities and determine optimal locations for new schools in planning educational services? Do time-based (15 min) benchmarks reveal different patterns than distance-only thresholds? The example of Płock shows the weaknesses of applying unified urban standards in areas with diverse types of spatial development and the need to modify them. To deepen the verification of the observed discrepancies, the study was extended to include an analysis based on the concept of the 15-Min City. The results revealed even greater disparities in accessibility, highlighting a strong contrast between central and peripheral districts. These findings remain consistent with the conclusions of international studies. Meanwhile, the applicable regulations in Poland provide relatively liberal accessibility thresholds. It may lead to an increase in the distance between residential development and educational facilities and other key elements of urban social infrastructure, thereby distancing national urban planning practices from the European principles of compact, 15 min, and sustainable cities. Full article

Text-dependent speaker verification (TD-SV), which verifies speaker identity using predefined phrases, has gained attention as a reliable contactless biometric authentication method for smart devices, internet of things (IoT), and real-time applications. However, in real-world environments, limited training data, background noise, and microphone channel variability significantly degrade TD-SV performance, particularly on resource-constrained devices that require real-time inference. To address these challenges, we propose a lightweight end-to-end TD-SV model based on a convolution-augmented transformer (Conformer) architecture enhanced with a channel-temporal attention (CTA) module as an input enhancement that specifically targets speaker-discriminative patterns in short, fixed utterances. Unlike existing attention mechanisms (Squeeze-and-Excitation Networks (SENet), Convolutional Block Attention Module (CBAM)) designed for computer vision tasks, our CTA module employs frequency-wise statistical pooling to capture acoustic variability patterns crucial for speaker discrimination within identical phonetic content. Experiments conducted on the challenging far-field and noisy SLR 85 HI-MIA dataset demonstrate that the proposed CTA-Conformer achieves an equal error rate (EER) of 2.04% and a minimum detection cost function (minDCF) of 0.20, achieving competitive performance compared to recent TD-SV approaches. Additionally, INT8 quantization reduces the model size by 75.8%, significantly improves inference speed, and enabling real-time deployment on edge devices. Our approach thus offers a practical solution for robust and efficient TD-SV in embedded internet of things (IoT) environments. Full article

In this study, the focus is on investigating the performance of Jatropha curcas biodiesel as a potentially eco-friendly and non-edible collector for use in the flotation of low-rank coal. Due to its high cost and limited efficiency, using diesel as a collector for treating low-rank coal flotation presents several challenges. To achieve this aim, a systematic approach was adopted, employing a statistical design methodology to develop comprehensive mathematical models for combustible recovery and ash content. These models considered various parameters, including the dosage of the collector and frother, the solid percentage, and the depressant. The test results indicated that both models were statistically significant (p < 0.05). Furthermore, the findings showed that when the collector, frother, solid percent, and depressant were set at 0.5 kg/t and 2.13 kg/t, 0.26 kg/t and 0.214 kg/t, 15.00% and 14.40%, and 0.50 kg/t and 0.51, respectively, the ash content and recovery efficiency were 11.2% and 80.08%, respectively. The results also indicated that the doses of the frother and collector had a greater impact on the response variables than the other factors. In addition, verification experiments were conducted under the ideal conditions specified by the models to assess their validity and sufficiency. The SEM-EDS results confirmed that the concentration of carbon in coal cleaned with Jatropha biodiesel was higher than that cleaned with diesel oil. Furthermore, an FT-IR investigation showed that Jatropha biodiesel was more effective than diesel oil in reducing hydrophilic groups and enhancing hydrophobic groups. The hydrogen bonding between the oxygen-containing groups in Jatropha biodiesel and the surface of low-rank coal was responsible for the improvement in floatability and flotation recovery, which means Jatropha biodiesel, could be utilized as a substitute collector in the flotation of low-rank coal. Full article

This article solves the anomalies’ operational detection in the network traffic problem for cyber police units by developing an adaptive neural network platform combining a variational autoencoder with continuous stochastic dynamics of the latent space (integration according to the Euler–Maruyama scheme), a continuous–discrete Kalman filter for latent state estimation, and Hotelling’s T² statistical criterion for deviation detection. This paper implements an online learning mechanism (“on the fly”) via the Euler Euclidean gradient step. Verification includes variational autoencoder training and validation, ROC/PR and confusion matrix analysis, latent representation projections (PCA), and latency measurements during streaming processing. The model’s stable convergence and anomalies’ precise detection with the metrics precision is ≈0.83, recall is ≈0.83, the F1-score is ≈0.83, and the end-to-end delay of 1.5–6.5 ms under 100–1000 sessions/s load was demonstrated experimentally. The computational estimate for typical model parameters is ≈5152 operations for a forward pass and ≈38,944 operations, taking into account batch updating. At the same time, the main bottleneck, the O(m³) term in the Kalman step, was identified. The obtained results’ practical significance lies in the possibility of the developed adaptive neural network platform integrating into cyber police units (integration with Kafka, Spark, or Flink; exporting incidents to SIEM or SOAR; monitoring via Prometheus or Grafana) and in proposing applied optimisation paths for embedded and high-load systems. Full article

The ornate dog tick Dermacentor reticulatus is a key vector of several pathogens and has been expanding its range across Europe, raising concerns about the associated veterinary and public health risks. This study aimed to assess the current distribution and local-scale expansion of D. reticulatus in southwestern Poland, particularly in and around the city of Wrocław. In 2024, host-seeking ticks were collected using the flagging method at 80 sites, including 30 previously monitored locations and 50 newly designated ones, selected based on land cover analysis and field verification. Spatial statistics and kriging method were applied to evaluate changes in the tick’s range compared to data from 2014–2019. The presence of D. reticulatus was confirmed at 68 sites, including 13 located beyond the previously estimated range. A shift in the mean center of tick occurrence toward the southeast was observed, along with an increase in the compact area of occurrence. The results indicate a continued expansion of D. reticulatus in the region, with urbanization and landscape structure likely influencing its spread. These findings underscore the importance of local-scale surveillance and spatial modeling in assessing the risk of tick-borne diseases. Full article