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Little is known about the exposure of aquatic biota to tire and road wear particles (TRWP) washed away from roads. Mussels were exposed for 7 days to model TRWP (m-TRWP), produced by milling tire tread particles with pure sand, and analyzed for 21 tire-related compounds by liquid chromatography-high resolution-mass spectrometry (LC-HRMS). Upon exposure to 0.5 g/L of m-TRWP, 15 compounds were determined from 944 μg/kg wet weight (diphenylguanidine, DPG) over 18 μg/kg for an oxidation product of N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6-PPDQ) to 0.6 μg/kg (4-hydroxydiphenyl amine). Transfer into mussels was highest for PTPD, DTPD and 6-PPDQ and orders of magnitude lower for 6-PPD. During 7 days depuration the concentration of all determined chemicals decreased to remaining concentrations between ~50 % (PTPD, DTPD) and 6 % (6-PPD). Suspect and non-target screening found 37 additional transformation products (TPs) of tire additives, many of which did not decrease in concentration during depuration, among them ten likely TPs of DPG, two of 6-PPD and PTPD and two of 1,2-dihydro-2,2,4-trimethylquinoline. A wide variety of chemicals is taken up by mussels upon exposure to m-TRWP and a wide range of TPs is formed, enabling the differentiation of biomarkers of exposure to TRWP and biomarkers of exposure to tire-associated chemicals.
2025
This study examines the environmental impacts of urban growth in Warsaw since 2006 and models the implications of future urban development for traffic pollutant emissions and pollution levels. Our findings demonstrate that, over the past two decades, urban sprawl has resulted in decreases in accessibility to public transport, social services, and natural areas. We analyse CO2 traffic emissions, NO2 concentrations, and population exposure across urban areas in future scenarios of further sprawling or alternative compacting land-use development. Results indicate that a compact future scenario reduces transport CO2 emissions and urban NO2 levels, though increases in population density raise exposure to air pollution. A sprawl future scenario increases CO2 and NOx emissions due to longer commutes and congestion, and NO2 levels increase up to 25% in parts of the city. Several traffic abatement strategies were simulated, and in all simulations a compact city consistently yields the largest reductions in CO2 emissions and NO2 levels, implying that the best abatement strategy for combating negative consequences of sprawl is to reduce sprawling. In both city layouts, network-wide improvements of public transport travel times gave significantly reduced emissions. Combined, our findings highlight the importance of co-beneficial urban planning strategies to balance CO2 emissions reduction, and air pollution exposure in expanding cities.
2025
A Nano Risk Governance Portal supporting risk governance of nanomaterials and nano-enabled products
isk governance (RG) of nanomaterials (NMs) has been at the focus of the Horizon 2020 Programme of the European Union, through the funding of three research projects (Gov4Nano, NANORIGO, RISKGONE). The extensive collaboration of the three projects, in various scientific topics, aimed to enhance RG of NMs and provide a solid scientific basis for effective collaboration of the various types of stakeholders involved. In this paper the development of a digital Nano Risk Governance Portal (NRGP) and associated information technology (IT) infrastructure supporting the risk governance of (engineered) nanomaterials and nano-enabled products, is presented, alongside considerations for future work and enhancement within the domain of Advanced Materials (AdMa). This paper describes several elements of this digital portal, which serves as a single-entry point for all stakeholders in need of, or interested in, nano-risk governance aspects. In its simplest form, the NRGP allows users to be efficiently guided towards tailored information about nanomaterials, risk governance concepts, guidance documents, harmonized methods for risk assessment, publicly accessible data, information and knowledge, as well as a directory of tools, to assess the exposure and hazard of nanomaterials and perform Safe-and-Sustainable-by-Design (SSbD) assessment in the context of nano-risk governance. This paper presents the technical implementation and the content of the first version of the NRGP alongside the vision for the future and further plans for development, implementation, hosting and maintenance of the NRGP aimed at ensuring its sustainability. This includes a procedure to link to, or include, currently available and future (nano)material-related (cloud) platforms, decision support systems, tools, guidance, and databases in line with good governance objectives.
2025
2025
Stochastic and deterministic processes in Asymmetric Tsetlin Machine
This paper introduces a new approach to enhance the decision-making capabilities of the Tsetlin Machine (TM) through the Stochastic Point Location (SPL) algorithm and the Asymmetric Steps technique. We incorporate stochasticity and asymmetry into the TM's process, along with a decaying normal distribution function that improves adaptability as it converges toward zero over time. We present two methods: the Asymmetric Probabilistic Tsetlin (APT) Machine, influenced by random events, and the Asymmetric Tsetlin (AT) Machine, which transitions from probabilistic to deterministic states. We evaluate these methods against traditional machine learning algorithms and classical Tsetlin (CT) machines across various benchmark datasets. Both AT and APT demonstrate competitive performance, with the AT model notably excelling, especially in complex datasets.
2025
Stress is a common human reaction to demanding circumstances, and prolonged and excessive stress can have detrimental effects on both mental and physical health. Heart rate variability (HRV) is widely used as a measure of stress due to its ability to capture variations in the time intervals between heartbeats. However, achieving high accuracy in stress detection through machine learning (ML), using a reduced set of statistical features extracted from HRV, remains a significant challenge. In this study, we aim to address these challenges by proposing lightweight ML models that can effectively detect stress using minimal HRV features and are computationally efficient enough for IoT deployment. We have developed ML models incorporating efficient feature selection techniques and hyper-parameter tuning. The publicly available SWELL-KW dataset has been utilized for evaluating the performance of our models. Our results demonstrate that lightweight models such as k-NN and Decision Tree can achieve competitive accuracy while ensuring lower computational demands, making them ideal for real-time applications. Promisingly, among the developed models, the k-nearest neighbors (k-NN) algorithm has emerged as the best-performing model, achieving an accuracy score of 99.3% using only three selected features. To confirm real-world deployability, we benchmarked the best model on an 8 GB NVIDIA Jetson Orin Nano edge device, where it retained 99.26% accuracy and completed training in 31 s. Furthermore, our study has incorporated local interpretable model-agnostic explanations to provide comprehensive insights into the predictions made by the k-NN-based architecture.
2025
Protein-nanomaterial interaction is a topic of great interest for nanotechnology research, particularly for advancing strategies in nanomedicine and nanosafety. This study explores the thermodynamic signatures associated with the interactions of six TiO2 nanoforms, (differing in their crystalline structure, surface properties and particle size) with bovine serum albumin as model protein. By integrating findings from electron paramagnetic resonance spectroscopy (EPR) regarding the free radical generation following interaction, together with information on the stability and conformational changes of the protein during adsorption on TiO2 nanomaterials, we aim to elucidate the binding mechanisms and identify the primary factors influencing nanomaterial's reactivity. The effect of the particle size, crystalline structure and surface properties on the binding parameters, protein structural stability and EPR data is discussed. Finally, the relevant parameters suitable for understanding molecular interactions at the bio/nano interface have been corroborated with the toxicological outcomes resulting from the measurements on the viability, proliferation and real time attachment of relevant cell lines, as well as with the detection of DNA strand breaks and oxidized DNA at the single-cell level. Thermodynamic and EPR parameters emerge as key descriptors for determining adsorption/binding processes and toxic effects of nanomaterials. The rankings with respect to cell damage and to oxidative stress inducing potential follow the same ranking seen in nanomaterial's influence on the BSA structural stability, binding affinity and enthalpic character of the interaction. Our findings highlight the intricate relationships between the parameters governing bio-nano interactions and the toxicity of the nanomaterials, and their significance in assessing nanomaterial safety and efficacy.
2025
2025
2025
Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks
In time-series data analysis, identifying anomalies is crucial for maintaining data integrity and ensuring accurate analyses and decision-making. Anomalies can compromise data quality and operational efficiency. The complexity of time-series data, with its temporal dependencies and potential non-stationarity, makes anomaly detection challenging but essential. Our research introduces ADSiamNet, a 1D Convolutional Neural Network-based Siamese network model for anomaly detection and rectification. ADSiamNet effectively identifies localized patterns in time-series data and smooths detected anomalies using a quantile-based technique. In tests with physical activity data from Actigraph watches and MOX2-5 sensors, ADSiamNet achieved accuracies of 98.65% and 85.0%, respectively, outperforming other supervised anomaly detection methods. The model uses a contrastive loss function to compare input sequences and adjusts network weights iteratively during training to recognize intricate patterns. Additionally, we evaluated various univariate time-series forecasting algorithms on datasets with and without anomalies. Results show that anomaly-smoothed data reduces forecasting errors, highlighting our approach’s effectiveness in enhancing time-series data analysis’s integrity and reliability. Future research will focus on multivariate time-series datasets.
2025