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Helicobacter pylori is a well-established causative agent of gastritis, peptic ulcers, gastric adenocarcinoma, and primary gastric lymphoma. It colonizes the human stomach and expresses numerous virulent factors that influence disease progression. Among these factors is the cytotoxin vacA gene, which encodes the vacuolating capacity of the cytotoxin and plays a key role in the bacterium’s pathogenic potential. This study investigated the allelic diversity of the vacA among H. pylori strains infecting patients in Jordan with various gastric conditions and examined potential associations between vacA s-and m- genotypes, histopathological and endoscopic findings, and the development of gastric diseases. Gastric biopsies were collected from 106 patients at two hospitals in Jordan who underwent endoscopic examination. The collected biopsies for each patient were subjected to histopathological assessment, urease detection using the Rapid Urease Test (RUT), a diagnostic test for H. pylori, and molecular detection of the vacA gene and its s and m alleles. The histopathology reports indicated that 83 of 106 patients exhibited gastric disorders, of which 81 samples showed features associated with H. pylori infection. The RUT was positive in 76 of 106 with an accuracy of 93.8%. Real-time polymerase chain reaction (RT-PCR) targeting the 16S rRNA gene confirmed the presence of H. pylori in 79 of 81 histologically diagnosed cases as infected (97.5%), while the vacA gene was detected only in 75 samples (~95%). To explore genetic diversity, PCR-amplified fragments underwent sequence analysis of the vacA gene. The m-allele was detected in 58 samples (73%), the s-allele was detected in 45 (57%), while both alleles were not detected in 13% of samples. The predominant genotype combination among Jordanians was vacA s2/m2 (50%), significantly linked to mild chronic gastritis, followed by s1/m2 (35%) and s1/m1 (11.8%) which are linked to severe gastric conditions including malignancies. Age-and gender-related differences in vacA genotype were observed with less virulent s2m2 and s1m2 genotypes predominating in younger adults specially males, while the more virulent m1 genotypes were found exclusively in females and middle-aged patients. Genomic sequencing revealed extensive diversity within H. pylori, likely reflecting its long-standing co-evolution with human hosts in Jordan. This genetic variability plays a key role in modulating virulence and influencing clinical outcomes. Comprehensive characterization of vacA genotypic variations through whole-genome sequencing is essential to enhance diagnostic precision, strengthen epidemiological surveillance, and inform targeted therapeutic strategies. While this study highlights the significance of the vacA m and s alleles, future research is recommended in order to investigate the other vacA allelic variations, such as the i, d, and c alleles, to achieve a more comprehensive understanding of H. pylori pathogenicity and associated disease severity across different strains. These investigations will be crucial for improving diagnostic accuracy and guiding the development of targeted therapeutic strategies. Full article

Small lakes (<10 km²) provide a range of ecosystem services but are often overlooked in both monitoring efforts and limnological studies. Remote sensing has been increasingly used to complement in situ monitoring or to provide water colour data for unmonitored inland water bodies. However, due to spatial, radiometric, and spectral constraints, it has been heavily focused on large lakes. Sentinel-2 MSI is the first sensor with the capability to consistently retrieve a wide range of essential water quality variables, such as chlorophyll-a concentration (chl-a) and water transparency, in small water bodies, and to provide long time series. Here, we provide and validate a framework for retrieving two variables, chl-a and turbidity, over lakes with diverse optical characteristics using Sentinel-2 imagery. It is based on GRS for atmospheric and sun glint correction, WaterDetect for water detection, and inversion models that were automatically selected based on two different sets of optical water types (OWTs)—one for each variable; for chl-a, we produced a blended product for improved spatial representation. To validate the approach, we compared the products with more than 600 in situ data from 108 lakes located in the Adour–Garonne river basins, ranging from 3 to ∼5000 ha, as well as remote sensing reflectance (Rrs) data collected during 10 field campaigns during the summer and spring seasons. Rrs retrieval (n = 65) was robust for bands 2 to 5, with MAPE varying from 15 to 32% and achieving correlation from 0.74 up to 0.92. For bands 6 to 8A, the Rrs retrieval was much less accurate, being influenced by adjacency effects. Glint removal significantly enhanced Rrs accuracy, with RMSE improving from 0.0067 to 0.0021 sr⁻¹ for band 4, for example. Water quality retrieval showed consistent results, with an MAPE of 56%, an RMSE of 11.4 mg m⁻³, and an r of 0.76 for chl-a, and an MAPE of 47%, an RMSE of 9.7 NTU, and an r of 0.87 for turbidity, and no significant effect of lake area or lake depth on retrieval errors. The temporal and spatial representations of the selected parameters were also shown to be consistent, demonstrating that the framework is robust and can be applied over lakes as small as 3 ha. The validated methods can be applied to retrieve time series of chl-a and turbidity starting from 2016 and with a frequency of up to 5 days, largely expanding the database collected by water agencies. This dataset will be extremely useful for studying the dynamics of these small freshwater ecosystems. Full article

This study evaluates the performance and energy trade-offs of three popular data processing libraries—Pandas, PySpark, and Polars—applied to GreenNav, a CO₂ emission prediction pipeline for urban traffic. GreenNav is an eco-friendly navigation app designed to predict CO₂ emissions and determine low-carbon routes using a hybrid CNN-LSTM model integrated into a complete pipeline for the ingestion and processing of large, heterogeneous geospatial and road data. Our study quantifies the end-to-end execution time, cumulative CPU load, and maximum RAM consumption for each library when applied to the GreenNav pipeline; it then converts these metrics into energy consumption and CO₂ equivalents. Experiments conducted on datasets ranging from 100 MB to 8 GB demonstrate that Polars in lazy mode offers substantial gains, reducing the processing time by a factor of more than twenty, memory consumption by about two-thirds, and energy consumption by about 60%, while maintaining the predictive accuracy of the model (R² ≈ 0.91). These results clearly show that the careful selection of data processing libraries can reconcile high computing performance and environmental sustainability in large-scale machine learning applications. Full article

This study analyses the potential of using sunflower husks as an energy source by producing bio-pellets and evaluating their combustion process in residential settings. As one of the leading sunflower producers in the European Union, Bulgaria generates significant agricultural residues with high, yet underutilized, energy potential. This study employs a combination of experimental data and numerical modelling aided by ANSYS 2024 R1 to analyse the combustion of sunflower husk pellets in a hot water boiler. The importance of balanced air distribution for achieving optimal combustion, reduced emissions, and enhanced thermal efficiency is emphasized by the results of a comparison of two air supply regimes. It was found that a secondary air-dominated air supply regime results in a more uniform temperature field and a higher degree of oxidation of combustible components. These findings not only confirm the technical feasibility of sunflower husk pellets but also highlight their commercial potential as a sustainable, low-cost energy solution for agricultural enterprises and rural heating providers. The research indicates that there are business-to-business (B2B) market opportunities for biomass producers, boiler manufacturers, and energy distributors who wish to align themselves with EU green energy policies and the growing demand for solutions that support the circular economy. Full article

This study pioneers the integration of Sentinel-2 satellite imagery with Kolmogorov–Arnold networks (KAN) for the evaluation of chlorophyll-a (Chl-a) concentrations in inland lakes. Using Baiyangdian Lake in Hebei Province, China, as a case study, a specialized KAN architecture was designed to extract spectral features from Sentinel-2 data, and a robust algorithm was developed for Chl-a estimation. The results demonstrate that the KAN model outperformed traditional feature-engineering-based machine learning (ML) methods and standard multilayer perceptron (MLP) deep learning approaches, achieving an R² of 0.8451, with MAE and RMSE as low as 1.1920 μg/L and 1.6705 μg/L, respectively. Furthermore, attribution analysis was conducted to quantify the importance of individual features, highlighting the pivotal role of bands B3 and B5 in Chl-a retrieval. Furthermore, spatio-temporal distributions of Chl-a concentrations in Baiyangdian Lake from 2020 to 2024 were generated leveraging the KAN model, further elucidating the underlying causes of water quality changes and examining the driving factors. Compared to previous studies, the proposed approach leverages the high spatial resolution of Sentinel-2 imagery and the accuracy and interpretability of the KAN model, offering a novel framework for monitoring water quality parameters in inland lakes. These findings may guide similar research endeavors and provide valuable decision-making support for environmental agencies. Full article

Sex-based immunological differences significantly influence the outcome of vaccination, yet the molecular mediators underpinning these differences remain largely elusive. MicroRNAs (miRNAs), key post-transcriptional regulators of gene expression, have emerged as critical modulators of innate and adaptive immune responses. In this study, we investigated the expression profile of selected circulating miRNAs as potential biomarkers of sex-specific humoral responses to the mRNA COVID-19 vaccine in a cohort of health care workers. Plasma samples were collected longitudinally at a defined time point (average 71 days) post-vaccination and analyzed using RT-qPCR to quantify a panel of immune-relevant miRNAs. Anti-spike (anti-S) IgG titers were measured by chemiluminescent immunoassays. Our results revealed sex-dependent differences in miRNA expression dynamics, with miR-221-3p and miR-148a-3p significantly overexpressed in vaccinated female HCWs and miR-155-5p overexpressed in vaccinated males. MiR-148a-3p showed a significant association with anti-S/RBD (RBD: receptor binding domain) IgG levels in a sex-specific manner. Bioinformatic analysis for miRNA targets indicated distinct regulatory networks and pathways involved in innate and adaptive immune responses, potentially underlying the differential immune activation observed between males and females. These findings support the utility of circulating miRNAs as minimally invasive biomarkers for monitoring and predicting sex-specific vaccine-induced immune responses and provide mechanistic insights that may inform tailored vaccination strategies. Full article

A previously synthesized and functionalized magnetic glycidyl methacrylate-based nanocomposite, mPGMT-deta, was tested as a sorbent for Re(VII) oxoanions in Mo(VI)-containing solutions. The effect of pH on the removal efficiency and the separation factor was examined in the range of 2 to 9. A maximum separation factor (β_Re/Mo) of 8.85 was observed at pH 6. The nature of rhenium oxoanions binding to the active sites of mPGMT-deta was analyzed using density functional theory (DFT). The calculations indicated that the formation of MoO₄²⁻//hedetaH₂²⁺ adduct is electrostatically favored at pH 6, while the inclusion of solvation effects makes the formation of ReO₄⁻//hedetaH₂²⁺ adduct thermodynamically more favorable. Solvation played a dominant role in determining the selectivity of oxoanion sorption to the nanocomposite. The adsorption isotherm, kinetics, and thermodynamics of Re(VII) onto mPGMT-deta were determined. The equilibrium data were best-fitted using the Langmuir adsorption model (R² = 0.999), with a maximum sorption capacity for Re(VII) of 0.43 mmol/g. The uptake kinetics of the sorption process obeyed the pseudo-second-order model, with the influence of diffusion and external mass transfer. Based on the thermodynamic parameters, Re(VII) sorption was spontaneous and endothermic. Full article

The structure of a scientific article is crucial for clearly conveying research findings. Modern scientific publications combine text with various elements—such as tables, graphs, images, diagrams, and maps—that support the narrative and aid data interpretation. Understanding how these components influence a publication’s reception and scientific impact is essential. This study analyzes differences among 15 soil science journals (indexed in the Web of Science) in terms of visual elements, tables, number of authors, and article length. The journals had a 5-year Impact Factor (2023) ranging from 0.9 (Soil and Environment) to 10.4 (Soil Biology and Biochemistry). The Kruskal–Wallis test and Bonferroni-adjusted Dunn’s post hoc tests revealed statistically significant differences across all variables (p < 0.05). The relationships were further assessed using Pearson’s correlation, based on the median number of authors and article length, as well as the percentage of articles that include at least one element of a given type (e.g., table, graph, image, diagram, or map). Key findings show that journals with a higher impact factor tend to publish articles with more authors (r = 0.62, p = 0.014), use diagrams more frequently (r = 0.69, p = 0.004), and include fewer tables (r = –0.85, p < 0.001). These results suggest that journals with a higher 5-year IF tend to include articles with a greater number of authors and a higher frequency of diagram use, while relying less on tables. Full article

TYC 622-742-1 and TYC 1193-1918-1 are evolved metal-poor (MP) high-speed stars with r-enhanced characteristics discovered in the Milky Way (MW) halo. The study of these halo stars is important for clarification of and knowledge about their origin. We employ the abundance decomposition method to fit the observed abundances of 25 elements in TYC 622-742-1 and 24 elements in TYC 1193-1918-1, representing the largest number of elements fitted in the current observed dataset. We analyze the astrophysical formation sites of both sample stars by calculating their abundance ratios and component ratios. The calculation results suggest that both stars originated in a gas cloud that was contaminated by the ejecta of primary and main r-process materials such as those from a neutron star merger (NSM), which enriched their heavy neutron-capture elements (HNCEs), and the material from the massive stars ($M\ge 10M_{\odot }$), which enriched their primary light, iron-group, and lighter neutron-capture elements (LNCEs). This implies that TYC 622-742-1 and TYC 1193-1918-1 are the main r-process-enhanced stars with strong primary-process contributions. We find that the component coefficients of the sample stars closely resemble those of metal-poor Galactic populations, indicating a probable origin within the MW. Furthermore, the $\alpha$-enhanced abundance patterns and orbital trajectories suggest that both stars likely formed in the Galactic disk, possibly within a globular cluster (GC), and were subsequently ejected into the halo through dynamical processes. Full article

This study focuses on producing and processing highly aligned tapes from recycled carbon fibers (rCFs). The rCFs are processed with a modified carding machine, oriented through a specialized subsequent process and consolidated into a semi-finished product. These rCF-tapes are placed onto a two-dimensional tool using an adapted automated fiber placement (AFP) technology to demonstrate a novel approach of producing composites from highly oriented recycled materials. The semi-finished stacks are consolidated in a heating press and test coupons are tested according to corresponding standards. The rCF-tapes are evaluated using methods such as tensile and flexural testing and determination of fiber volume content. Mechanical values are assessed by processing various generations of rCF-tapes and comparing them to each other and to virgin fiber tapes (vCF-tapes) made of the same type of carbon fiber and matrix. Microscopic imaging is also performed to analyze the quality of the resulting composites. In this study, a tensile strength of up to 1100 MPa in the fiber direction and stiffness of up to 80 GPa at a fiber volume content (FVC) of approximately 40% were achieved. The results highlight the strong potential and benefits of using highly oriented rCF-tapes and demonstrate the suitability of fiber placement technologies for those recycled materials. Full article

To address the complex pile–soil interaction mechanisms in predicting the compressive bearing capacity of HSCM piles (Helix Stiffened Cement Mixing piles) in marine soft soil regions, this study proposes an intelligent prediction method based on a GA-BPNN (Genetic Algorithm-Optimized Back Propagation Neural Network). A high-quality database comprising 1243 data points was established through finite element numerical simulations. By integrating data preprocessing techniques and the GA-BPNN model, the study systematically investigated the influence of helical blade spacing H₁ and H₂, strength ratio C_ref/S_u, and diameter ratio D_sc/D_H on bearing capacity. The results demonstrate that the GA-BPNN model achieves a prediction accuracy of 99.07%, with a mean squared error (MSE) of 7.20 × 10⁻³ and a coefficient of determination R² of 0.990. SHAP value analysis reveals that the strength ratio and diameter ratio are the dominant factors, exhibiting nonlinear relationships with bearing capacity characterized by saturation effects and threshold-dependent behavior. Laboratory tests further confirm strong correlations between cement–soil strength C_ref, formed pile diameter D_sc, and bearing capacity. The findings indicate that the GA-BPNN model provides an efficient and accurate approach for predicting the bearing capacity of HSCM piles, offering a reliable basis for engineering parameter optimization. Full article

This paper provides geospatial and correlation analysis of heavy metal distribution in the soil cover of the city of Temirtau and its industrial zones. Based on 25 soil samples taken in 2024, concentrations of nine heavy metals (As, Pb, Zn, Cu, Ni, Co, Mn, Cr, Ba) were determined using X-ray fluorescence analysis. Spatial data interpolation was performed using the Kriging method in the ArcGIS Pro environment. The results showed the presence of localized extreme pollution zones, primarily near the Qarmet JSC metallurgical plant. The most significant exceedances of maximum permissible concentrations (MPC), up to 348× MPC for Cr, 160× MPC for Zn, and 72× MPC for As, were recorded at individual locations. Correlation analysis revealed a moderate positive relationship between several elements, particularly Mn and Cu (r = 0.64). Comparison of the spatial distribution of pollution with population data allowed for the assessment of potential environmental risks. This research emphasizes the need to implement systematic monitoring, sustainable land management practices, ecological maps, and preventive measures to reduce the long-term impact of heavy metals on ecosystems and public health, and to promote environmental sustainability in industrial regions. Full article

The balance between exploration and exploitation is a fundamental element in the design and performance of bio-inspired optimization algorithms. However, to date, its conceptual evolution and its treatment in the scientific literature have not been systematically characterized from a bibliometric approach. This study performs an exhaustive analysis of the scientific production on the balance between exploration and exploitation using records extracted from the Web of Science (WoS) database. The processing and analysis of the data were carried out through the combined use of Bibliometrix (R package) and VOSviewer, tools that made it possible to quantify productivity, map collaborative networks, and visualize emerging thematic trends. The results show a sustained growth in the volume of publications over the last decade, as well as the consolidation of academic collaboration networks and the emergence of new thematic lines in the field. In particular, metaheuristic algorithms have demonstrated a significant and growing impact, constituting a fundamental pillar in the advancement and methodological diversification of the exploration–exploitation balance. This work provides a quantitative framework and a structured view of the evolution of research, identifies the main actors and trends, and raises opportunities for future lines of research in the field of optimization using metaheuristics, the most prominent instantiation of bio-inspired optimization algorithms. Full article

Acute Respiratory Distress Syndrome (ARDS) is a complex and devastating form of respiratory failure, with high mortality rates, for which there is no pharmacological treatment. The acute exudative phase of ARDS is characterized by severe damage to the alveolar–capillary barrier, infiltration of protein-rich fluid into the lungs, neutrophil recruitment, and high levels of inflammatory mediators. Rapid resolution of this reversible acute phase, with efficient restoration of alveolar functional integrity, is essential before the establishment of irreversible fibrosis and respiratory failure. Several lines of in vitro and in vivo evidence support the involvement of potassium (K⁺) channels—particularly KvLQT1, expressed in alveolar cells—in key cellular mechanisms for ARDS resolution, by promoting alveolar fluid clearance and epithelial repair processes. The aim of our study was to investigate whether pharmacological activation of KvLQT1 channels could elicit beneficial effects on ARDS parameters in an animal model of acute lung injury. We used the well-established bleomycin model, which mimics (at day 7) the key features of the exudative phase of ARDS. Our data demonstrate that treatments with the KvLQT1 activator R-L3, delivered to the lungs, failed to improve endothelial permeability and lung edema in bleomycin mice. However, KvLQT1 activation significantly reduced neutrophil recruitment and tended to decrease levels of pro-inflammatory cytokines/chemokines in bronchoalveolar lavages after bleomycin administration. Importantly, R-L3 treatment was associated with significantly lower injury scores, higher levels of alveolar type I (HTI-56, AQP5) and II (pro-SPC) cell markers, and improved alveolar epithelial repair capacity in the presence of bleomycin. Together, these results suggest that the KvLQT1 K⁺ channel may be a potential target for the resolution of the acute phase of ARDS. Full article

Assessment is fundamental to programming education; however, it is a labour-intensive and complicated process, especially in extensive learning contexts where it relies significantly on human teachers. This paper presents an automated grading methodology designed to assess Python programming exercises, producing both continuous and discrete grades. The methodology incorporates GPT-4-Turbo, a robust large language model, and machine learning models selected by PyCaret’s automated process. The Extra Trees Regressor demonstrated superior performance in continuous grade prediction, with a Mean Absolute Error (MAE) of 4.43 out of 100 and an R² score of 0.83. The Random Forest Classifier attained the highest scores for discrete grade classification, achieving an accuracy of 91% and a Quadratic Weighted Kappa of 0.84, indicating substantial concordance with human-assigned categories. These findings underscore the promise of integrating LLMs and automated model selection to facilitate scalable, consistent, and equitable assessment in programming education, while substantially alleviating the workload on human evaluators. Full article