00208nas a2200085   4500000000100000000000100001008004100002100002400043245005500067       2013                            d1 aPrzemysław Sekuła00aKolejowe przewozy regionalne–wyzwania i problemy00237nas a2200097   4500000000100000000000100001008004100002100002400043245006500067490000700132       2014                            d1 aPrzemysław Sekuła00aZarządzanie programami według Program Management Institute0 v1200252nas a2200097   4500000000100000000000100001008004100002100002400043700001100067245007600078       2015                            d1 aPrzemysław Sekuła1 aothers00aBudżet zadaniowy jako skuteczne narzędzie zarządzania w samorządzie00382nas a2200121   4500000000100000000000100001008004100002100002300043700002900066700001800095700002400113245012300137       2016                            d1 aHenryk Brandenburg1 aKatarzyna Ficek-Wojciuch1 aMarek Magdoń1 aPrzemysław Sekuła00aInteresariusze projektów publicznych–sukces projektu publicznego w ujęciu specjalistów od zarządzania projektami00395nas a2200121   4500000000100000000000100001008004100002100002300043700002900066700001800095700002400113245013600137       2016                            d1 aHenryk Brandenburg1 aKatarzyna Ficek-Wojciuch1 aMarek Magdoń1 aPrzemysław Sekuła00aProjekt badawczy pt" Sukces projektu publicznego i jego uwarunkowania"-wyniki studiów literaturowych i program realizacji projektu00352nas a2200133   4500000000100000000000100001008004100002100002400043700002300067700001800090700002900108245007400137490000700211       2016                            d1 aPrzemysław Sekuła1 aHenryk Brandenburg1 aMarek Magdoń1 aKatarzyna Ficek-Wojciuch00aMulticriteria evaluation methods in location of touristic investments0 v5000340nas a2200133   4500000000100000000000100001008004100002100001100043700001800054700001900072700002400091700001800115245007300133       2018                            d1 aYi Hou1 aStanley Young1 aKaveh Sadabadi1 aPrzemysław Sekuła1 aDenise Markow00aEstimating highway volumes using vehicle probe data-proof of concept00381nas a2200133   4500000000100000000000100001008004100002100002400043700002100067700001700088700001900105245011600124490000700240       2018                            d1 aPrzemysław Sekuła1 aNikola Marković1 aZachary Laan1 aKaveh Sadabadi00aEstimating historical hourly traffic volumes via machine learning and vehicle probe data: A Maryland case study0 v9700434nas a2200145   4500000000100000000000100001008004100002100002100043700002400064700001700088700002200105700002200127245013200149490000700281       2018                            d1 aNikola Marković1 aPrzemysław Sekuła1 aZachary Laan1 aGennady Andrienko1 aNatalia Andrienko00aApplications of trajectory data from the perspective of a road transportation agency: Literature review and maryland case study0 v2000397nas a2200133   4500000000100000000000100001008004100002100002400043700001700067700001900084700002600103245012500129490000900254       2018                            d1 aPrzemysław Sekuła1 aZachary Laan1 aKaveh Sadabadi1 aMirosław Skibniewski00aPredicting work zone collision probabilities via clustering: application in optimal deployment of highway response teams0 v201800397nas a2200157   4500000000100000000000100001008004100002100001400043700001800057700001600075700002400091700001500115700001500130245008600145490000800231       2019                            d1 aYing Zhou1 aChenshuang Li1 aLieyun Ding1 aPrzemysław Sekuła1 aPeter Love1 aCheng Zhou00aCombining association rules mining with complex networks to monitor coupled risks0 v18600337nas a2200121   4500000000100000000000100001008004100002100002400043700002100067700001700088700001900105245009100124       2018                            d1 aPrzemysław Sekuła1 aNikola Marković1 aZachary Laan1 aKaveh Sadabadi00aApplication of Vehicle Probe Data in Estimating Traffic Volumes: a Maryland Case Study00389nas a2200133   4500000000100000000000100001008004100002100001800043700002400061700001800085700001700103245012600120490000900246       2020                            d1 aSara Zahedian1 aPrzemysław Sekuła1 aAmir Nohekhan1 aZachary Laan00aEstimating hourly traffic volumes using artificial neural network with additional inputs from automatic traffic recorders0 v267400396nas a2200157   4500000000100000000000100001008004100002100001500043700001300058700001600071700002400087700001400111700001600125245008900141490000800230       2020                            d1 aCheng Zhou1 aBin Tang1 aLieyun Ding1 aPrzemysław Sekuła1 aYing Zhou1 aZekun Zhang00aDesign and automated assembly of Planetary LEGO Brick for lunar in-situ construction0 v11800318nas a2200133   4500000000100000000000100001008004100002100001300043700001400056700002400070700001600094245006800110490000600178       2021                            d1 aYayin Xu1 aYing Zhou1 aPrzemysław Sekuła1 aLieyun Ding00aMachine learning in construction: From shallow to deep learning0 v600425nas a2200145   4500000000100000000000100001008004100002100002400043700001700067700001900084700002000103700001800123245012900141490000900270       2021                            d1 aPrzemysław Sekuła1 aZachary Laan1 aKaveh Sadabadi1 aKrzysztof Kania1 aSara Zahedian00aTransferability of a Machine Learning-Based Model of Hourly Traffic Volume Estimation—Florida and New Hampshire Case Study0 v202100401nas a2200157   4500000000100000000000100001008004100002260001000043100001500053700001800068700001900086700001700105700001900122700002400141245007800165       2019                            d  bITSWC1 aMark Franz1 aStanley Young1 aStephen Remias1 aMichael Pack1 aKaveh Sadabadi1 aPrzemysław Sekuła00aAutomated Traffic Signal Performance Measures Using Emerging Data Sources00231nas a2200085   4500000000100000000000100001008004100002100002400043245007800067       2022                            d1 aPrzemysław Sekuła00aSzacowanie natężenia ruchu drogowego z wykorzystaniem sieci neuronowych00232nas a2200097   4500000000100000000000100001008004100002100002400043700001100067245005600078       2016                            d1 aPrzemysław Sekuła1 aothers00aWpływ autostrad na rozwój lokalny–wyniki badań00340nas a2200133   4500000000100000000000100001008004100002100001100043700001800054700001900072700002400091700001800115245007300133       2018                            d1 aYi Hou1 aStanley Young1 aKaveh Sadabadi1 aPrzemysław Sekuła1 aDenise Markow00aEstimating highway volumes using vehicle probe data-proof of concept00397nas a2200157   4500000000100000000000100001008004100002100001400043700001800057700001600075700002400091700001500115700001500130245008600145490000800231       2019                            d1 aYing Zhou1 aChenshuang Li1 aLieyun Ding1 aPrzemysław Sekuła1 aPeter Love1 aCheng Zhou00aCombining association rules mining with complex networks to monitor coupled risks0 v18600337nas a2200121   4500000000100000000000100001008004100002100002400043700002100067700001700088700001900105245009100124       2018                            d1 aPrzemysław Sekuła1 aNikola Marković1 aZachary Laan1 aKaveh Sadabadi00aApplication of Vehicle Probe Data in Estimating Traffic Volumes: a Maryland Case Study00396nas a2200157   4500000000100000000000100001008004100002100001500043700001300058700001600071700002400087700001400111700001600125245008900141490000800230       2020                            d1 aCheng Zhou1 aBin Tang1 aLieyun Ding1 aPrzemysław Sekuła1 aYing Zhou1 aZekun Zhang00aDesign and automated assembly of Planetary LEGO Brick for lunar in-situ construction0 v11800318nas a2200133   4500000000100000000000100001008004100002100001300043700001400056700002400070700001600094245006800110490000600178       2021                            d1 aYayin Xu1 aYing Zhou1 aPrzemysław Sekuła1 aLieyun Ding00aMachine learning in construction: From shallow to deep learning0 v600317nas a2200121   4500000000100000000000100001008004100002100001800043700002000061700001500081700002400096245007500120       2018                            d1 aMasoud Hamedi1 aSepideh Eshragh1 aMark Franz1 aPrzemysław Sekuła00aAnalyzing impact of I-85 bridge collapse on regional travel in Atlanta00457nas a2200157   4500000000100000000000100001008004100002260001200043100002400055700002400079700002300103700002000126700002500146245009100171856003700262       2024                            d  c06/20241 aMichał Romaszewski1 aPrzemysław Sekuła1 aPrzemysław Głomb1 aMichał Cholewa1 aKatarzyna Kołodziej00aThrough the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models  uhttps://arxiv.org/abs/2406.0492600429nas a2200157   4500000000100000000000100001008004100002100001200043700001200055700002400067700001200091700001000103245011200113856004000225490000600265       2024                            d1 aC. Zhou1 aR. Chen1 aPrzemysław Sekuła1 aB. Tang1 aY. Qu00aHuman–machine interaction controlling system for teleoperation of robotic arms in prefabrication assembly  uhttps://doi.org/10.55092/sc202400050 v100409nas a2200145   4500000000100000000000100001008004100002100002500043700002000068700002400088700002300112700001900135700002400154245008500178       2024                            d1 aKatarzyna Kołodziej1 aMichał Cholewa1 aMichał Romaszewski1 aPrzemysław Głomb1 aWojciech Koral1 aPrzemysław Sekuła00aCalibration of Water Demand Network Through a Machine Learned Digital Twin Model00305nas a2200109   4500000000100000000000100001008004100002260002800043100002400071700002400095245007600119       2024                            d  c04/2024aWarsaw, Poland1 aMichał Romaszewski1 aPrzemysław Sekuła00aPoster: Explainable classification of multimodal time series using LLMs00427nas a2200157   4500000000100000000000100001008004100002260001200043100002400055700002400079700002300103700002000126700002500146245009100171490000700262       2025                            d  c03/20251 aMichał Romaszewski1 aPrzemysław Sekuła1 aPrzemysław Głomb1 aMichał Cholewa1 aKatarzyna Kołodziej00aThrough the Thicket: A Study of Number-Oriented LLMS Derived from Random Forest Models0 v1501940nas a2200157   4500000000100000000000100001008004100002100002400043700002400067700002300091700002000114700001900134245006100153856006700214520150100281       2025                            d1 aPrzemysław Sekuła1 aMichał Romaszewski1 aPrzemysław Głomb1 aMichał Cholewa1 aŁukasz Pawela00aQuantum-aware Transformer model for state classification  uhttps://link.springer.com/chapter/10.1007/978-3-031-97570-7_153 a<p>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.</p>
00474nas a2200169   4500000000100000000000100001008004100002100002400043700002100067700001600088700001800104700002100122700002000143245008900163856004500252490000700297       2025                            d1 aPrzemysław Sekuła1 aNarjes Shayesteh1 aQinglian He1 aSara Zahedian1 aRodrigo Moscosso1 aMichał Cholewa00aMove Over Law Compliance Analysis Utilizing a Deep Learning Computer Vision Approach  uhttps://www.mdpi.com/2076-3417/15/4/20110 v1501834nas a2200217   4500000000100000000000100001008004100002260001200043100002400055700001600079700001900095700002100114700001800135700001700153700001500170700002000185245007100205856004600276490000700322520128700329       2025                            d  c09/20251 aPrzemysław Sekuła1 aQinglian He1 aKaveh Sadabadi1 aRodrigo Moscosso1 aThomas Jacobs1 aZachary Laan1 aMark Franz1 aMichał Cholewa00aVehicle-to-Infrastructure System Prototype for Intersection Safety  uhttps://www.mdpi.com/2076-3417/15/17/97540 v153 a<p>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.</p>
02630nas a2200241   4500000000100000000000100001008004100002260001200043100002000055700002200075700002300097700002400120700001900144700002400163700002000187700002500207700001900232700002100251245012200272490000700394520197300401022001402374       2025                            d  c09/20251 aKamil Książek1 aWilhelm Masarczyk1 aPrzemysław Głomb1 aMichał Romaszewski1 aKrisztian Buza1 aPrzemysław Sekuła1 aMichał Cholewa1 aKatarzyna Kołodziej1 aPiotr Gorczyca1 aMagdalena Piegza00aDeep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals0 v213 a<p>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.</p>
  a1553-735800416nas a2200157   4500000000100000000000100001008004100002260001200043100001800055700001500073700001800088700002400106700001700130700002100147245009000168       2025                            d  c11/20251 aSara Zahedian1 aMark Franz1 aDharia Parekh1 aPrzemysław Sekuła1 aMichael Pack1 aCharles Lattimer00aAssessment of the Impact of the Francis Scott Key Bridge Collapse on Regional Traffic00582nas a2200193   4500000000100000008004100001100002400042700001700066700002500083700001800108700002500126700002300151700002000174700002300194700002100217700002400238700001600262245011000278       2026                            d1 aMichał Romaszewski1 aKamil Drejer1 aKatarzyna Kołodziej1 aAnna Zawadzka1 aStanisław Lewiński1 aPrzemysław Głomb1 aMarek Ruciński1 aMichał Krupiński1 aKrzysztof Gryguc1 aPrzemysław Sekuła1 aSzymon Sala00aToward Seasonal Guidelines for Robust Deep-Learning Sentinel-2 Building Detection in Different Area Types00626nas a2200193   4500000000100000008004100001100001800042700002600060700002000086700002400106700002400130700002000154700001800174700002000192700002200212700002100234245017000255490000700425       2026                            d1 aJarosław Bis1 aKinga Gościńska-Bis1 aŁukasz Mokrisz1 aMikołaj Jodłowski1 aPrzemysław Sekuła1 aMichał Cholewa1 aKrystian Wita1 aMarta Strugała1 aRyszard Bachowski1 aRadosław Gocoł00aArtificial Intelligence-Enhanced Remote Monitoring After Cardiac Surgery: A Prospective Single-Centre Feasibility Study with Retrospective Wound Image Classification0 v1500470nas a2200145   4500000000100000008004100001100001600042700001500058700001500073700002400088245011900112856007200231490000700303022001400310       2026                            d1 aJiajing Liu1 aJianxin Wu1 aHanbin Luo1 aPrzemysław Sekuła00aA knowledge distillation framework for multi-signal-to-noise ratio speech enhancement in construction environments  uhttps://www.sciencedirect.com/science/article/pii/S26661659260011580 v26  a2666-1659