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font="default" size="100%">Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">Wpływ autostrad na rozwój lokalny–wyniki badań</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Yi Hou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Stanley Young</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kaveh Sadabadi</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Denise Markow</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Estimating highway volumes using vehicle probe data-proof of concept</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">Estimating highway volumes using vehicle probe data-proof of concept</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Ying Zhou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Chenshuang Li</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Lieyun Ding</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Peter Love</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Cheng Zhou</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Combining association rules mining with complex networks to monitor coupled risks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2019</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%"/></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">186</style></VOLUME><title><style face="normal" font="default" size="100%">Combining association rules mining with complex networks to monitor coupled risks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Nikola Marković</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zachary Laan</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kaveh Sadabadi</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Application of Vehicle Probe Data in Estimating Traffic Volumes: a Maryland Case Study</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">Application of Vehicle Probe Data in Estimating Traffic Volumes: a Maryland Case Study</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Cheng Zhou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bin Tang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Lieyun Ding</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ying Zhou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zekun Zhang</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Design and automated assembly of Planetary LEGO Brick for lunar in-situ construction</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2020</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Automation in Construction</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">118</style></VOLUME><title><style face="normal" font="default" size="100%">Design and automated assembly of Planetary LEGO Brick for lunar in-situ construction</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Yayin Xu</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ying Zhou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Lieyun Ding</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Machine learning in construction: From shallow to deep learning</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Developments in the built environment</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">6</style></VOLUME><title><style face="normal" font="default" size="100%">Machine learning in construction: From shallow to deep learning</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Masoud Hamedi</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Sepideh Eshragh</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mark Franz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Analyzing impact of I-85 bridge collapse on regional travel in Atlanta</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2018</style></YEAR><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">Analyzing impact of I-85 bridge collapse on regional travel in Atlanta</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Michał Romaszewski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michał Cholewa</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">arXiv preprint arXiv:2406.04926</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><DATE><style face="normal" font="default" size="100%">06/2024</style></DATE><URL><style face="normal" font="default" size="100%">https://arxiv.org/abs/2406.04926</style></URL><title><style face="normal" font="default" size="100%">Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">C. Zhou</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">R. Chen</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">B. Tang</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Y. Qu</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Human–machine interaction controlling system for teleoperation of robotic arms in prefabrication assembly</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Smart Construction</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">1</style></VOLUME><URL><style face="normal" font="default" size="100%">https://doi.org/10.55092/sc20240005</style></URL><title><style face="normal" font="default" size="100%">Human–machine interaction controlling system for teleoperation of robotic arms in prefabrication assembly</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michał Cholewa</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michał Romaszewski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Wojciech Koral</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Calibration of Water Demand Network Through a Machine Learned Digital Twin Model</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">5th Polish Conference on Artificial Intelligence (PP-RAI 2024)</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><title><style face="normal" font="default" size="100%">Calibration of Water Demand Network Through a Machine Learned Digital Twin Model</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Michał Romaszewski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Poster: Explainable classification of multimodal time series using LLMs</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">5th Polish Conference on Artificial Intelligence (PP-RAI 2024)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Warsaw, Poland</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><DATE><style face="normal" font="default" size="100%">04/2024</style></DATE><title><style face="normal" font="default" size="100%">Poster: Explainable classification of multimodal time series using LLMs</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Michał Romaszewski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michał Cholewa</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Through the Thicket: A Study of Number-Oriented LLMS Derived from Random Forest Models</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Journal of Artificial Intelligence and Soft Computing Research</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">15</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">3</style></ISSUE><SECTION><style face="normal" font="default" size="100%">279</style></SECTION><DATE><style face="normal" font="default" size="100%">03/2025</style></DATE><title><style face="normal" font="default" size="100%">Through the Thicket: A Study of Number-Oriented LLMS Derived from Random Forest Models</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michał Romaszewski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michał Cholewa</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Łukasz Pawela</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Quantum-aware Transformer model for state classification</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">International Conference on Computational Science 2025</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Entanglement is a fundamental feature of quantum mechanics, playing a crucial role in quantum information processing. However, classifying entangled states, particularly in the mixed-state regime, remains a challenging problem, especially as system dimensions increase. In this work, we focus on bipartite quantum states and present a data-driven approach to entanglement classification using transformer-based neural networks. Our dataset consists of a diverse set of bipartite states, including pure separable states, Werner entangled states, general entangled states, and maximally entangled states. We pretrain the transformer in an unsupervised fashion by masking elements of vectorized Hermitian matrix representations of quantum states, allowing the model to learn structural properties of quantum density matrices. This approach enables the model to generalize entanglement characteristics across different classes of states. Once trained, our method achieves near-perfect classification accuracy, effectively distinguishing between separable and entangled states. Compared to previous Machine Learning, our method successfully adapts transformers for quantum state analysis, demonstrating their ability to systematically identify entanglement in bipartite systems. These results highlight the potential of modern machine learning techniques in automating entanglement detection and classification, bridging the gap between quantum information theory and artificial intelligence.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-031-97570-7_15</style></URL><title><style face="normal" font="default" size="100%">Quantum-aware Transformer model for state classification</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Narjes Shayesteh</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Qinglian He</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Sara Zahedian</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Rodrigo Moscosso</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michał Cholewa</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Move Over Law Compliance Analysis Utilizing a Deep Learning Computer Vision Approach</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Applied Sciences</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">15</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">4</style></ISSUE><URL><style face="normal" font="default" size="100%">https://www.mdpi.com/2076-3417/15/4/2011</style></URL><title><style face="normal" font="default" size="100%">Move Over Law Compliance Analysis Utilizing a Deep Learning Computer Vision Approach</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Qinglian He</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kaveh Sadabadi</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Rodrigo Moscosso</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Thomas Jacobs</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Zachary Laan</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mark Franz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michał Cholewa</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Vehicle-to-Infrastructure System Prototype for Intersection Safety</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Applied Sciences</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">15</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">17</style></ISSUE><DATE><style face="normal" font="default" size="100%">09/2025</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;This study investigates the use of Autonomous Sensing Infrastructure and Connected and Autonomous Vehicles (CAV) technologies to support infrastructure-to-vehicle (I2V) and infrastructure-to-everything (I2X) communications, including the alerting of drivers and pedestrians. It describes research findings in the following CAV functionalities: (1) Intersection-based object detection and tracking; (2) Basic Safety Message (BSM) generation and transmission; and (3) In-Vehicle BSM receipt and display, including handheld (smartphone) application BSM receipt and user presentation. The study summarizes the various software and hardware components used to create the I2V and I2X prototype solutions, which include open-source and commercial software as well as industry-standard transportation infrastructure hardware, e.g., Signal Controllers. Results from in-lab testing demonstrate effective object detection (e.g., pedestrians, bicycles) based on sample traffic camera video feeds as well as successful BSM message generation and receipt using the leveraged software and hardware components. The I2V and I2X solutions created as part of this research are scheduled to be deployed in a real-world intersection in coordination with state and local transportation agencies.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://www.mdpi.com/2076-3417/15/17/9754</style></URL><title><style face="normal" font="default" size="100%">Vehicle-to-Infrastructure System Prototype for Intersection Safety</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Kamil Książek</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Wilhelm Masarczyk</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michał Romaszewski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Krisztian Buza</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michał Cholewa</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Gorczyca</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Magdalena Piegza</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">PLOS Computational Biology</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">21</style></VOLUME><SECTION><style face="normal" font="default" size="100%">1</style></SECTION><DATE><style face="normal" font="default" size="100%">09/2025</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Schizophrenia and bipolar disorder are severe mental illnesses that significantly impact quality of life. These disorders are associated with autonomic nervous system dysfunction, which can be assessed through heart activity analysis. Heart rate variability (HRV) has shown promise as a potential biomarker for diagnostic support and early screening of those conditions. This study aims to develop and evaluate an automated classification method for schizophrenia and bipolar disorder using short-duration electrocardiogram (ECG) signals recorded with a low-cost wearable device. We conducted classification experiments using machine learning techniques to analyze R-R interval windows extracted from short ECG recordings. The study included 60 participants—30 individuals diagnosed with schizophrenia or bipolar disorder and 30 control subjects. We evaluated multiple machine learning models, including Support Vector Machines, XGBoost, multilayer perceptrons, Gated Recurrent Units, and ensemble methods. Two time window lengths (about 1 and 5 minutes) were evaluated. Performance was assessed using 5-fold cross-validation and leave-one-out cross-validation, with hyperparameter optimization and patient-level classification based on individual window decisions. Our method achieved classification accuracy of 83% for the 5-fold cross-validation and 80% for the leave-one-out scenario. Despite the complexity of our scenario, which mirrors real-world clinical settings, the proposed approach yielded performance comparable to advanced diagnostic methods reported in the literature. The results highlight the potential of short-duration HRV analysis as a cost-effective and accessible tool for aiding in the diagnosis of schizophrenia and bipolar disorder. Our findings support the feasibility of using wearable ECG devices and machine learning-based classification for psychiatric screening, paving the way for further research and clinical applications.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Sara Zahedian</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mark Franz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Dharia Parekh</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michael Pack</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Charles Lattimer</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Assessment of the Impact of the Francis Scott Key Bridge Collapse on Regional Traffic</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Transportation Research Record: Journal of the Transportation Research Board</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><DATE><style face="normal" font="default" size="100%">11/2025</style></DATE><title><style face="normal" font="default" size="100%">Assessment of the Impact of the Francis Scott Key Bridge Collapse on Regional Traffic</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Michał Romaszewski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kamil Drejer</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Katarzyna Kołodziej</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Anna Zawadzka</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Stanisław Lewiński</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Głomb</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Marek Ruciński</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michał Krupiński</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Krzysztof Gryguc</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Szymon Sala</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Toward Seasonal Guidelines for Robust Deep-Learning Sentinel-2 Building Detection in Different Area Types</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2026</style></YEAR><title><style face="normal" font="default" size="100%">Toward Seasonal Guidelines for Robust Deep-Learning Sentinel-2 Building Detection in Different Area Types</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Jarosław Bis</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kinga Gościńska-Bis</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Łukasz Mokrisz</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mikołaj Jodłowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Przemysław Sekuła</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Michał Cholewa</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Krystian Wita</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Marta Strugała</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ryszard Bachowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Radosław Gocoł</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Artificial Intelligence-Enhanced Remote Monitoring After Cardiac Surgery: A Prospective Single-Centre Feasibility Study with Retrospective Wound Image Classification</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2026</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Journal of Clinical Medicine</style></SECONDARY_TITLE><VOLUME><style 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