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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
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
Are ingredients of personal care products likely to undergo long-range transport to remote regions?
Personal care products (PCPs) contain contaminants of emerging concern. Despite increasing reports of their presence in polar regions, the behavior of PCP ingredients under cold environmental conditions remains poorly understood. Snow collected around Villum Research Station at Station Nord, Greenland, between December 2018 and June 2019 was extracted in a stainless steel clean-room and analyzed for seven fragrance materials, four organic UV-filters and an antioxidant using gas chromatography-tandem mass spectrometry. All twelve target PCPs were detected, with elevated concentrations during two sampling events potentially tied to air mass transport from northern Europe and the northern coasts of Russia. To contextualize the presence of these PCP chemicals in high Arctic snow, we estimated their (i) partitioning properties as a function of temperature, (ii) equilibrium phase distribution and dominant deposition processes in the atmosphere at temperatures above and below freezing, and (iii) potential for long-range environmental transport (LRET). Even though most PCPs are deemed to be gas phase chemicals predominantly deposited as vapors, rapid atmospheric degradation is expected to limit their LRET. On the other hand, the less volatile octocrylene is expected to be sorbed to atmospheric particles, removed via wet and dry particle deposition, and possibly exhibit a higher potential for LRET by being protected from attack by photooxidants. The contrast between consistent detection of PCP chemicals in high Arctic snow and relatively low estimated LRET potential emphasizes the need for further research on their real-world atmospheric behavior under cold conditions.
2025