Advancing Open Science

The development of eco-friendly antimicrobial materials is essential for addressing antibiotic resistance, while reducing environmental impact. In this study, bio-derived anionic and cationic cellulose nanofibers (a-CNF and c-CNF) were employed as templating matrices for the in situ hydrothermal synthesis of cellulose/ZnO nanohybrids. Physicochemical characterization confirmed efficient cellulose functionalization and high-quality nanofibrillation, as well as the formation of uniformly dispersed ZnO nanoparticles (≈10–20 nm) strongly integrated within the cellulose network. The ZnO content was 30 and 20 wt. % for a-CNF/ZnO and c-CNF/ZnO, respectively. Antibacterial evaluation against Escherichia coli and Staphylococcus aureus revealed enhanced activity for both hybrids, with c-CNF/ZnO displaying the lowest MIC/MBC values (50/100 μg/mL). Antiviral assays revealed complete feline calicivirus inactivation at 100 μg/mL for c-CNF/ZnO, while moderate activity was observed against bovine coronavirus, highlighting the role of surface charge. Cytotoxicity assays on mammalian cells demonstrated high biocompatibility at antimicrobial concentrations. Life cycle assessment showed that c-CNF/ZnO exhibits a lower overall environmental burden than a-CNF/ZnO, with electricity demand being the main contributor, indicating clear opportunities for further reductions through process optimization and scale-up. Overall, these results demonstrate that CNF/ZnO nanohybrids effectively combine renewable biopolymers with ZnO antimicrobial functionality, offering a sustainable and safe platform for biomedical and environmental applications.

Carbon farming represents a strategic approach to enhancing agricultural sustainability while reducing greenhouse gas (GHG) emissions. In Türkiye, agriculture accounted for approximately 14.9% of national GHG emissions in 2023, dominated by methane (CH₄) and nitrous oxide (N₂O). By increasing carbon storage in soils and vegetation, carbon farming can improve soil health, water retention, and climate resilience, thereby contributing to mitigation efforts and sustainable rural development. This study reviews and synthesizes international and national evidence on carbon farming mechanisms, practices, payment models, and adoption enablers and barriers, situating these insights within Türkiye’s agroecological and institutional context. The analysis draws on a systematic review of peer-reviewed literature, institutional reports, and policy documents published between 2015 and 2025. The findings indicate substantial mitigation potential from soil-based practices and livestock- and manure-related measures, yet limited uptake due to low awareness, capacity constraints, financial and administrative barriers, and regulatory gaps, highlighting the need for region-specific approaches. To support implementation and scaling, the study proposes a policy-oriented, regionally differentiated and digitally enabled MRV framework and an associated implementation pathway designed to reduce transaction costs, enhance farmer participation, and enable integration with emerging carbon market mechanisms.

Quality control of drinking water is essential for safeguarding public health, particularly in densely populated urban environments. Environmental microbiological monitoring can complement conventional surveillance by providing deeper insights into the dissemination of pathogens and antimicrobial resistance genes within aquatic systems. In this study, we assessed the quality of wastewater and treated water from two urban water supply systems, representing the southern and northern regions of Porto Alegre, Rio Grande do Sul, Brazil, across four climatic seasons between 2024 and 2025. Fifteen water samples were analyzed, including raw water from Guaíba Lake and treated water collected from public distribution points. The Water Quality Index was calculated, microbiological indicators were quantified, and carbapenem resistance genes were detected using molecular assays. Most treated water samples complied with established bacteriological standards; however, the bla_OXA-48-like gene was recurrently detected in both wastewater and treated water. No resistance genes were identified during the summer, whereas the bla_VIM gene was detected exclusively in spring samples. The presence of carbapenem resistance genes in the absence of cultivable coliforms suggests the persistence of extracellular DNA or viable but non-culturable bacteria, highlighting limitations inherent to conventional microbiological monitoring. Integrating classical microbiological methods with molecular assays enables a more comprehensive assessment of water quality and strengthens evidence-based decision-making within a One Health framework.

The accurate evaluation of the Joint Roughness Coefficient (JRC) is crucial for rock mechanics engineering. Existing JRC prediction models based on a single fractal parameter often face limitations in physical consistency and predictive accuracy. This study proposes a novel two-parameter JRC prediction method based on fractal topology theory. The core innovation of this method lies in extracting two distinct types of information from a roughness profile: the scale-invariant characteristics of its frequency distribution, quantified by the Hurst exponent (H), and the amplitude-dependent scale effects, quantified by the coefficient (C). By integrating these two complementary aspects of roughness, a comprehensive predictive model is established: JRC = $100.014H^{-1.5491}{C}^{1.2681}$. The application of this model to Atomic Force Microscopy (AFM)-scanned coal rock surfaces indicates that JRC is primarily controlled macroscopically by amplitude-related information (reflected by C), while the scale-invariant frequency characteristics (reflected by H) significantly influence local prediction accuracy. By elucidating the distinct roles of scale-invariance and amplitude attributes in controlling JRC, this research provides a new theoretical framework and a practical analytical tool for the quantitative evaluation of JRC in engineering applications.

While infectious diseases represent a daunting challenge to public health worldwide, their impact is disproportionately felt among the most vulnerable and marginalized segments of society [...]

Choy sum (Brassica rapa var. parachinensis) is an important vegetable crop in Brassicaceae. However, its mitochondrial genome has not been well studied. In this study, Illumina and Nanopore sequencing technologies were combined to assemble the complete mitochondrial genome of choy sum. The mitochondrial genome is a circular molecule of 219,775 bp, with a GC content of 45.23%. A total of 60 genes were annotated, including 33 protein-coding genes (PCGs), 23 transfer RNA (tRNA) genes, 3 ribosomal RNA (rRNA) genes, and one pseudogene. A total of 466 RNA editing sites were identified in the PCGs. Codon usage analysis revealed that leucine (leu) was the most frequently used amino acid. Twenty-nine codons showed a relative synonymous codon usage (RSCU) value greater than 1. Most of these preferred codons ended with A or U. A total of 308 repetitive sequences were detected, including 136 dispersed repeats, 17 tandem repeats, and 55 simple sequence repeats (SSRs). Evolutionary analysis indicated that most mitochondrial genes are under negative selection. The highest nucleotide diversity detected in the cox2 gene suggests that this gene could serve as a valuable molecular marker for mitochondrial research in the species. Homology analysis found 22 homologous fragments between the mitochondrial and chloroplast genomes of choy sum. These fragments total 13,325 bp, representing 6.06% of the mitochondrial genome. Phylogenetic analysis showed that choy sum is most closely related to B. rapa var. purpuraria. This study offers a genomic resource for genetic improvement and breeding of choy sum. It also provides molecular insights into the evolution of Brassica species.

A comfortable, reliable, safe and environmentally friendly high-speed train journey that saves time and offers an unforgettable experience for passengers is not a dream. Passengers can enjoy panoramic views, delicious cuisine and use their mobile devices without restrictions. High-speed trains, powered by environmentally friendly methods, are a sustainable form of transport, reducing harmful emissions. Integrating intelligent control and management into railway networks has the capacity to increase efficiency and improve reliability and safety, as well as reduce development and maintenance costs. Future intelligent railway network architectures are expected to focus on integrated, multi-layered systems that deeply embed artificial intelligence (AI), the Internet of Things (IoT) and advanced communication technologies (5G/6G) to ensure intelligent operation, improved reliability and increased safety. Distributed intelligent control in railways refers to an advanced approach in which decision-making capabilities are distributed across network components (trains, stations, track sections, control centers) rather than being concentrated in a single central location. The recent advances in AI in railways are associated with numerous scientific papers that enable intelligent traffic management, automatic train control, and predictive maintenance, with each of the proposed intelligent solutions being evaluated in terms of key performance indicators such as latency, reliability, and accuracy. This study focuses on how different intelligent solutions in railways can be implemented in network components based on the requirements for real-time control, near-real-time control, and non-real-time operation. The analysis of related works is focused on the proposed intelligent railway frameworks and architectures. The description of typical use cases for implementing intelligent control aims to summarize latency requirements and the possible distribution of control logic between network components, taking into account time constraints. The considered use case of automatic train protection aims to evaluate the added latency of communication. The requirements for the nodes that host and execute the control logic are identified.

This study analyzes artificial intelligence development and green economic efficiency across 31 Chinese provinces using 2019–2021 panel data. We apply the entropy weight TOPSIS method to measure AI development levels. The entropy weight TOPSIS method measures AI development levels, the DEA-BCC model assesses green economic efficiency, and their coordination types are identified. Findings reveal a significant negative correlation between AI development and green economic efficiency. We explain this complex relationship through three mechanisms: short-term polarization effects, technology conversion lags, and spatial spillovers. Spatial analysis shows AI development forms high-high agglomerations in the Yangtze River Delta and Shandong. Green economic efficiency shows high-high clustering in the Beijing-Tianjin-Hebei region and selected western provinces. Using a “two-system” coupling framework, we identify four provincial categories. The “double-high” type should function as growth poles. The “high-low” type requires improved technology conversion efficiency. The “low-high” type can leverage ecological advantages. The “double-low” type needs enhanced factor inputs. We propose three targeted policy recommendations: establishing digital-green synergy platforms, implementing inter-provincial AI resource collaboration mechanisms, and developing locally adapted action plans.