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Found 6515 publications
Antimicrobial use and Escherichia coli resistance patterns in Hungarian pig farms: a data-driven farm-level analysis
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
Antimicrobial resistance (AMR) poses a critical challenge to both human and veterinary medicine, with pig production recognized as one of the major contributor due to intensive antimicrobial usage (AMU). This study aimed to explore the relationship between AMU and AMR patterns of Escherichia coli isolated from commercial pig farms, using data-driven analytical methods. Farm-level records were harmonized with microbiological data from 203 isolates collected in December 2023 across four Hungarian farms. AMU was summarized over 3-, 6-, 9-, and 12-month retrospective windows and expressed in modified population-corrected units, while AMR was quantified as mean minimum inhibitory concentration (MIC) and AMR rate under epidemiological and clinical breakpoints. The results revealed substantial variation in AMU among farms, with amoxicillin predominating across timeframes. Farm-specific comparisons indicated that higher AMU may not always coincide with elevated resistance levels, and data analysis did not consistently identify a direct association between use and resistance at the individual farm level, which warrants further investigation in larger datasets. Correlation analyses identified strong intra-class relationships among β-lactams and fluoroquinolones, as well as a cross-class linking, suggesting concurrent selection pressures. Overall, the integration of AMU and AMR data demonstrated the feasibility of farm-level surveillance for AMR modelling and provides a foundation for future predictive systems to support antimicrobial stewardship in livestock production.
Open Access: Yes
Discovery of potential antiviral compounds and accelerating the therapeutic discovery against monkeypox virus
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
Monkeypox virus is a zoonotic virus of the genus Orthopox viruses. It can be transmitted through direct or indirect contact with animals or infected ones. Owing similarity of pathogenesis with smallpox, the same drugs can be used for both viruses, but they are not specific and only help to relieve the symptoms only. Therefore, the absence of antiviral treatment or licensed vaccine highlights an urgent need, especially due to its rapid prevalence. The study screened the library of compounds to retrieve drug-like molecules that can act against monkeypox virus. The highly virulent target gene B8R having uniport ID Q3I8J0 was chosen. Targeting B8R is substantial for global health and can align with SDG 3 and awareness of disease management. The B8R was modelled via Artificial intelligence (AI) AlphaFold method and then exposed to a library of compounds. Complementary interactions in the active site were shown by molecular docking. The Complex-1 had the greatest binding affinity (–8.4 kcal/mol), followed by Complex-2 (–8.1 kcal/mol) and Complex-3 (–7.7 kcal/mol). After 125 ns, Complex-1 reached equilibrium at 7.5 Å RMSD, according to MD simulations, exhibiting stable ligand retention and reliable interactions with crucial residues Gly135 and Lys136. Complex-3 shown intermediate protein stability (6 Å RMSD) but notable ligand fluctuation (48 Å RMSF), while Complex-2 displayed increased protein RMSD (8 Å RMSD) and delayed ligand stabilisation (16 Å RMSF). These results were corroborated by PCA analysis, which showed that Complex-1 exhibits coherent structural development whereas Complex-2 and Complex-3 show scattered and compact trajectories, respectively. Complex-1 promise for Mpox viral inhibition was highlighted by the fact that it was the most stable and dynamically favourable contender overall. The N-terminal follows the folding trend. The insilico analysis not only proposed a potent compound but also provides deep insight into the behavior of protein. The proposed potent compound against this zoonotic virus can be helpful to combat the monkeypox virus by subjecting it further towards experimental investigation.
Open Access: Yes
Mitigation of inductive coupling effects on buried pipelines using gradient control conductors of overhead line configuration and hippopotamus optimization algorithm
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
By electromagnetic perturbation effect, the extra-high voltage (EHV) overhead transmission lines can cause significant induced voltages and currents on buried metallic pipelines located in the immediate vicinity. These voltages can present a source of hazard both for the structural I ntegrity of the metallic pipeline and for the safety of personnel responsible for operation and maintenance. This paper proposes the quasi-static modeling of the electromagnetic interference to which a buried metallic pipeline will be subjected nearby an extra-high voltage (EHV) overhead transmission line, under steady-state operating conditions of the power electrical grid. Using the electrical network analysis method to evaluate the induced voltage levels and its effects on the buried pipeline; also, to propose a mitigation strategy if necessary. The results obtained show that the values of the AC induced voltage on the buried pipeline are significant and exceed the limits defined by the international NACE standard. They can cause a risk of electrocution for intervention personnel and accelerate the process of metal corrosion. Therefore, the gradient control mitigation technique of the conductors and their optimal geometric arrangement of EHV transmission line using Hippopotamus Optimization (HO) algorithm were proposed to reduce AC induced voltages within the permissible safety limits, according to the requirements of the NACE Standard. Finally, it should be noted that the implementation of these mitigation approaches have led to remarkable results in eliminating potential risks.
Open Access: Yes
Real-time monitoring of ammonia emissions from cereal crops using LoRaWAN-based sensing technology
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
This study presents a LoRaWAN-based IoT system developed for real-time monitoring of ammonia (NH₃) emissions in cereal crop fields. Sustainable agriculture increasingly demands on-farm greenhouse gas (GHG) tracking linked to environmental variables. IoT offers efficient real-time monitoring of soil NH₃ emissions and associated factors. Our research introduces a unique Field Monitoring Laboratory: a LoRaWAN-connected IoT system integrating soil, crop, and microclimate sensors to observe NH₃⁺, air temperature, rainfall, humidity, soil temperature, and moisture content. The system comprises a field lab, data server, and custom dashboard with analytics capabilities. NH₃ fluxes were measured in autumn-sown cereals across three growing seasons (2020–2023). Tukey’s Kramer test revealed significant (p < 0.05, p < 0.001) differences in NH₃ emissions and environmental variables between years. Highest NH₃ emissions (1.94 ppm in 2020, 1.71 ppm in 2021) coincided with elevated air (25–31 °C) and soil (21–23 °C) temperatures, and higher mean and peak rainfall (0.40–0.48 mm average; max 9–31.6 mm). Principal Component Analysis showed 65.8% variance explained by PC1 and PC2, with high loadings from temperature and soil moisture. Spearman’s correlation indicated moderate positive associations (r = 0.38–0.4, p < 0.05) of NH₃ with soil moisture at 20 cm and 40 cm of soil depth, and a weak negative correlation (r = -0.16 and − 0.17) with soil temperature at 20 cm and 40 cm. The study underscores the potential of IoT technology using calibrated gas sensors and LoRaWAN for real-time NH₃ and environmental monitoring, enabling informed decision-making in smart agriculture.
Open Access: Yes
Assessing accessibility barriers at public transport stops for people with disabilities: Study in Hungary
Publication Name: European Transport Studies
Publication Date: 2026-12-01
Volume: 3
Issue: Unknown
Page Range: Unknown
Description:
Public transport systems play an instrumental role in promoting mobility, independence and the equitable participation of all individuals in daily activities Despite accessibility having become a fundamental component within transport planning, a significant proportion of public transport systems continue to exhibit notable barriers, thus impeding the mobility of individuals with disabilities to a considerable extent. The present study employs a qualitative methodology to evaluate the accessibility of the public transport system in the city of Győr, Hungary. This evaluation is based on field study, photographic documentation, and a user survey, the latter of which was conducted to support the findings derived from the qualitative methods employed. The analysis identifies four main categories of accessibility barriers: pedestrian environment barriers, stop infrastructure barriers, transport-information barriers, and vehicle-boarding barriers. The findings indicate that inadequate pedestrian environments, poorly designed stops, insufficient tactile and auditory guidance, and difficulties in boarding vehicles significantly restrict accessibility for users with disabilities. This paper proposes practical recommendations and solutions that have the potential to enhance accessibility and ensure compliance with accessibility standards.
Open Access: Yes
Ensemble deep learning approach for traffic video analytics in edge computing
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
Video analytics is the new era of computer vision in identifying and classifying objects. Traffic surveillance videos can be analysed to using computer vision to comprehend the road traffic. Monitoring the real-time road traffic is essential to control them. Computer vision helps in identifying the vehicles on the road, but the present techniques either perform the video analysis on the cloud platform or the edge platform. The former introduces more delay in processing while controlling is needed in real-time, the latter is not accurate in estimating the current road traffic. YOLO algorithms are the most notable ones for efficient real-time object detection. To make such object detections feasible in lightweight environments, its tinier version called Tiny YOLO is used. Edge computing is the efficient framework to have its computation done on the edge of the physical layer without the need to move data into the cloud to reduce latency. A novel hybrid model of vehicle detection and classification using Tiny YOLO and YOLOR is constructed at the edge layer. This hybrid model processes the video frames at a higher rate and produces the traffic estimate. The numerical traffic volume is sent to Ensemble Learning in Traffic Video Analytics (ELITVA) which uses F-RNN to make decisions in reducing the traffic flow seamlessly. The experimental results performed on drone dataset captured at road signals show an increase in precision by 13.8%, accuracy by 4.8%, recall by 17.4%, F1 score by 19.9%, and frame rate processing by 12.8% compared to other existing traffic surveillance systems and efficient controlling of road traffic.
Open Access: Yes
Microplastic pollution in the Szigetköz section of the Danube: sources, composition and FTIR-based quantification
Publication Name: Environmental Systems Research
Publication Date: 2026-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Microplastic (MP) pollution in river systems has become an increasing environmental concern, particularly in transboundary rivers such as the Danube. This study provides the first detailed assessment of microplastic contamination in the Szigetköz section of the Danube and its major tributary, the Mosoni-Danube. Depth-resolved pumped water samples were collected at three locations (Rajka, Mecsér and Gönyű) and analysed using Fourier Transform Infrared (FTIR) spectroscopy combined with automated spectral evaluation. MP concentrations showed a clear downstream increase, with average values of 83.8 particles/m³ at Rajka, 237.6 particles/m³ in the Mosoni-Danube at Mecsér, and 795.9 particles/m³ at Gönyű. Polyethylene (PE) was the dominant polymer in the tributary (70.6%), whereas both PE and alkyd resins were prevalent at the main Danube sites (Rajka: alkyd 37.7%, PE 31.8%; Gönyű: alkyd 39.9%, PE 37.3%). Particle size distribution also shifted downstream, with a higher proportion of smaller (50–100 μm) particles detected at Gönyű compared to upstream sites. The results suggest that the tributary may represent an important input to the main Danube channel in this section, while differences in polymer composition point to varying source characteristics within the study area. These findings provide an important baseline for future monitoring and support the development of targeted mitigation strategies in this transboundary river system.
Open Access: Yes
Categorisation of SDG targets into ESG pillars based on ESRS taxonomy
Publication Name: Discover Sustainability
Publication Date: 2026-12-01
Volume: 7
Issue: 1
Page Range: Unknown
Description:
This study examines the alignment between the Sustainable Development Goals (SDGs) and the Environmental, Social, and Governance (ESG) pillars through the lens of the European Sustainability Reporting Standards (ESRS) taxonomy, complemented by the Global Reporting Initiative (GRI). The research introduces a policy-relevant framework that categorizes SDG targets within ESG pillars, offering structured guidance for policymakers and regulatory bodies to harmonize global sustainability goals with corporate reporting practices. By mapping 199 GRI and 201 ESRS accounting entries to the 17 SDGs, the study identifies significant opportunities to address thematic and procedural gaps in existing reporting systems. The findings demonstrate that SDG 8 (“Decent Work and Economic Growth”) exhibits the highest linkage rate to ESRS accounting items, reinforcing its relevance for policy-driven frameworks that integrate economic resilience with social equity. This harmonized approach underscores the role of policy in fostering alignment between corporate ESG strategies and broader sustainability objectives, mitigating greenwashing risks, and advancing standardization across regions and sectors. The study advocates for policy interventions that leverage this framework to enhance transparency, accountability, and long-term decision-making for sustainable development.
Open Access: Yes
Hybrid ML and metaheuristic optimization of slag-fly ash-gypsum modified solidified sludge for construction
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
Conventional sludge disposal, including incineration and landfilling, is unsustainable and can cause secondary pollution; thus, sludge solidification is emerging as a sustainable alternative. This study aims to combine machine learning (ML) and metaheuristic optimization to maximize the unconfined compressive strength (UCS) of municipal sludge modified with slag, desulfurized gypsum, and fly ash. A total of 190 specimens were tested, and predictive models based on Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Regression (SVR), LightGBM, XGBoost, CatBoost, K-Nearest Neighbors (KNN), and Histogram Gradient Boosting (HistGBoost) were coupled with the Whale Optimization Algorithm (WOA). In addition, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Gazelle optimization algorithm (GOA), Octopus Optimization Algorithm (OOA), Hiking Optimization Algorithm (HOA), and Young’s double-slit experiment optimizer (YDSE) were applied for comparison. Sensitivity analysis identified optimal WOA–ML parameter settings. The results demonstrated that the WOA–RF model outperformed all metaheuristic and other WOA–ML approaches by achieving the highest predicted UCS (8.29851 MPa). The WOA-ML models yielded an average optimal mix comprising sludge (44.2%), gypsum (19%), slag (18.7%), fly ash (16%), and NaOH (2.1%). Among the metaheuristic algorithms, PSO, GOA, OOA, TJO, DOA, GA, and YDSE demonstrated competitive performance. GWO achieved the highest UCS (8.226109 MPa), while HOA yielded the lowest (5.15366 MPa). The optimal mix averaged 38.9% sludge, 23.7% gypsum, 21.6% fly ash, 13.4% slag, and 2.5% NaOH. Partial dependence analysis confirmed the nonlinear effects of these parameters, while SHAP sensitivity analysis validated the optimization results. RSM validation further confirmed that both WOA–ML and metaheuristic approaches reliably predict the optimal UCS of modified sludge.
Open Access: Yes
