Advancing Open Science

The integration of electric buses into urban transportation networks is a priority for policymakers aiming to promote sustainable public mobility. Among available technologies, electric Bus Rapid Transit (eBRT) systems offer an environmentally friendly and operationally effective alternative to conventional modes. This study introduces a Machine Learning Decision Support Framework designed to assess the feasibility of deploying eBRT systems in urban environments. Using a dataset of 28 routes in the Athens Metropolitan Area, the framework integrates diverse variables such as land use, population coverage, proximity to public transport, points of interest, road characteristics, and safety indicators. The XGBoost model demonstrated strong predictive performance, outperforming traditional approaches and highlighting the significance of points of interest, land use diversity, green spaces, and roadway infrastructure in forecasting travel times. Overall, the proposed framework provides urban planners and policymakers with a robust, data-driven tool for evaluating the practical and environmental viability of eBRT systems.

This paper introduces a novel SEIRS-type differential model that incorporates significant real-world factors such as vaccination, hospitalization, and vital dynamics. The model is described by a system of nonlinear ordinary differential equations with time-dependent parameters and coefficients. First, fundamental biological properties of the model, including the existence, uniqueness, and non-negativity of its solution, are established. In addition, using official COVID-19 data from Bulgaria, a special inverse problem for the differential model is formulated and investigated through the construction of an appropriate family of time-discrete inverse problems. As a result, the model parameters are identified, and the model is validated using real-world data. The presented numerical experiments confirm that the proposed methodology performs well in real-world applications with actual data. A very good agreement between computed and officially reported data with respect to the $l_2$ and $l_{\infty }$ norms is obtained. The model and its simulation tools are adaptable and can be applied to datasets from other countries, provided suitable epidemiological data are available.

Restoring soil microbial functioning in reclaimed coal gangue soils is critical for ecosystem recovery, yet how different organic amendments, particularly industrial by-products, regulate bacterial communities remains unclear. Here, we tested three organic inputs—the residue after evaporation (RAE) from vitamin C production, Trichoderma inoculation, and cattle manure—applied alone and in combination in a photovoltaic agroforestry system on coal gangue spoil. Our results indicate that the treatment based on manure increased bacterial α-diversity and favored taxa associated with organic matter transformation, including Actinobacteria and Acidobacteriota, suggesting expanded niche partitioning in response to heterogeneous substrates and nutrients. RAE alone supported communities closer to non-manure controls but, when co-applied with manure, further enhanced network connectivity and the prevalence of positive associations, indicating strengthened cooperative interactions and functional redundancy. In contrast, RAE combined with Trichoderma in the absence of manure reduced diversity, and simplified the co-occurrence network, suggesting resource monopolization and antagonism. Overall, RAE acted as a key driver of microbial cooperation and potential ecosystem resilience, and RAE-based amendments, particularly when integrated with manure, appear to be effective strategies for improving soil microbial functionality in degraded coal gangue soils.

As a typical arid region, the change in Xinjiang’s vegetation carbon footprint is crucial for assessing ecological restoration and resource allocation. This study analyzes the changes in the vegetation carbon footprint and its influencing factors in Xinjiang by employing a range of models, including Net Ecosystem Productivity (NEP), carbon emission fitting, carbon footprint analysis, and structural equation modeling (SEM). Furthermore, using the carbon deficit vegetation investment estimation method, we quantify the additional vegetation area and investment required for Xinjiang to achieve carbon neutrality. The results show the following: (1) Net Ecosystem Productivity (NEP) increased slowly, with six regions (Altay, Bortala, Bayingolin, Kizilsu Kirghiz, Tacheng, and Yili) contributing 66.95% of the total NEP, forming the main carbon sink. Meanwhile, carbon emissions rose significantly, coming largely from Urumqi, Changji, Kumul, and Karamay (61.31% of total emissions). (2) The carbon footprint expanded 3.44 times, from 30.41 × 10⁴ km² to 104.49 × 10⁴ km². Human activities were the main positive driver, while vegetation factors negatively influenced the carbon footprint. (3) Based on the 21-year average carbon deficit, achieving carbon neutrality in Xinjiang requires an estimated investment of USD 106.77 × 10⁸ to expand cropland, woodland, and grassland by 8029 km², 1710 km², and 35,016 km², respectively. Implementing vegetation expansion, improving carbon markets, and transforming carbon-source economies are essential to achieving the “double carbon” goal. This study clarifies regional carbon sources/sinks and supports the carbon neutrality strategy in arid ecosystems.

Accurate Prognostics and Health Management (PHM) of cutting tools in Computer Numerical Control (CNC) milling machines is essential for minimizing downtime, improving product quality, and reducing maintenance costs. Previous studies have frequently applied deep learning, particularly Long Short-Term Memory (LSTM) neural networks, for tool wear prediction and Remaining Useful Life (RUL) prediction. However, they often rely on simplified datasets or single architectures limiting industrial relevance. This study proposes a novel ensemble-LSTM framework that combines LSTM, BiLSTM, Stacked LSTM, and Stacked BiLSTM architectures using a GRU-based meta-learner to exploit their complementary strengths. The framework is evaluated using the publicly available PHM’2010 milling dataset, a well-established industrial benchmark comprising comprehensive time-series sensor measurements collected under variable loads and realistic machining conditions. Experimental results show that the ensemble-LSTM outperforms individual LSTM models, achieving an RMSE of 2.4018 and an MAE of 1.9969, accurately capturing progressive tool wear trends and adapting to unseen operating conditions. The approach provides a robust, reliable solution for real-time predictive maintenance and demonstrates strong potential for industrial tool condition monitoring.

The transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) plays a critical role in regulating cellular defense against oxidative stress and maintaining redox homeostasis. In the context of viral infections, Nrf2 signaling emerges as a double-edged sword. On one hand, it activates a broad spectrum of antioxidant and cytoprotective genes, contributing to host defense and antiviral immunity. On the other hand, certain viruses exploit the Nrf2 pathway to create a favorable environment for replication, persistence, or immune evasion. This review summarizes the current understanding of Nrf2’s antiviral and proviral roles in both RNA and DNA virus infections, delineates the underlying mechanisms, and discusses the therapeutic implications of targeting Nrf2. We emphasize the need for context-dependent modulation of Nrf2 activity and highlight future directions in precision antiviral strategies.

In this study, the effects of pediocin (PP) on intestinal barrier function, renal injury, and immune regulation were evaluated in Salmonella pullorum-infected chickens. Forty-five 7-day-old specific-pathogen-free (SPF) chickens were randomly assigned to three groups: control (CON), S. pullorum infection (SP), and S. pullorum infection + PP treatment (SPA). The results showed that S. pullorum infection significantly elevated (p < 0.05) the renal (CREA, UREA), hepatic (ALT, AST), immunological (IgG, IgM), and inflammatory (TNF-α, IL-6, SAA, CRP) parameters, as well as the expression of trefoil factor 3, Toll-like receptor 2, TNF-α, IL-1β, and IL-6. In contrast, the jejunal villus height and the villus-to-crypt ratio, and the expression of intestinal tight junction proteins (occludin, claudin-1, and Zonula occludens-1), mucin-2, and transforming growth factor-β1 were significantly decreased in both the SP and SPA groups. In the SP group, the parameter alterations observed at 6 DPI compared to the CON group persisted until 12 DPI. In contrast, in the SPA group, these parameters returned to levels comparable to those of the CON group after 6 days of PP treatment. Moreover, S. pullorum infection markedly reduced the α-diversity of the gut microbiota, and this reduction could be partially restored following PP treatment. At the phylum level, S. pullorum infection significantly reduced the relative abundances of Proteobacteria and Verrucomicrobia. PP treatment increased the abundances of Firmicutes and Actinobacteria, while also restoring the abundances of Proteobacteria and Verrucomicrobia to some extent. At the genus level, PP treatment significantly increased the abundance of Faecalibacterium and Lactobacillus. Additionally, Faecalibacterium and Butyricicoccus were significantly more abundant in the SPA group. Thus, PP could alleviate S. pullorum infection induced intestinal barrier damage, reduce immune stress responses, and exert a protective effect by modulating the composition of the intestinal microbiota of chickens.

Nintedanib is an anti-fibrotic medication endowed with a multi-kinase inhibitor profile and approved for the treatment of Idiopathic Pulmonary Fibrosis (IPF). Nintedanib is believed to inhibit mainly Vascular Endothelial Growth Factor (VEGF), Platelet-Derived Growth Factor (PDGF), and Fibroblast Growth Factor (FGF) receptor kinases. The main objective was to identify potential tissue and/or circulating biomarkers to demonstrate Nintedanib’s target engagement and support its in vivo pharmacodynamic activity, consistent with its proposed mechanism(s) of action. In four independent experiments of bleomycin (BLM)-induced lung fibrosis model in rats, animals received Nintedanib (oral, 100 mg/kg/day) from day 7 post-BLM for 3 weeks. As expected, Nintedanib significantly reduced lung weight, the levels of lung fibrotic markers, and fibrotic areas. Moreover, Nintedanib-treated animals expressed lower levels of FGF2 in lung homogenates and higher plasma and lung levels of VEGF (≥3-fold, p < 0.05) compared to control animals. Lung proteomic analysis revealed the inhibition of receptor tyrosine kinases signaling in Nintedanib-treated animals. Circulating and lung levels of Nintedanib confirmed an optimal tissue distribution in the rat, consistent with the data reported for humans. Although VEGF ligand levels are elevated in the lungs of Nintedanib-treated animals, the VEGF signaling pathway remained functionally downregulated, strongly suggesting compensatory VEGF feedback delivery to its receptor blockade by Nintedanib. In summary, based on the present experimental findings in rats and supporting clinical preliminary evidence, increased VEGF levels can be reasonably considered an indicator of target engagement for Nintedanib and potentially for other VEGF modulators.