12 research outputs found
Iterate averaging methods for solving non-linear programming problems: Applied to a transportation network equilibrium problem
No full textTraffic congestion is an unresolved problem and it has effects not only to the transportation system, but also on other aspects of life (economic, spatial and social). The idea is that 'Road Pricing' can be used to solve this problem. There is a need for an appropriate tool for predicting the effects of 'Road Pricing'. Such a tool could be a traffic assignment model. Traffic is by nature dynamic and hence only dynamic models can describe traffic process adequately. It appears, however, that iterate averaging methods have not yet been applied to transportation network problems. In this research iterate averaging methods are investigated and also the possibility of applying these methods in transportation network problems. Recently the Polyak method was introduced, which is supposed to have better convergence qualities than the method that is normally used, the Method of Successive Averages (MSA). The three topics of this research of iterate averaging methods are: \u95 To find out how the Polyak method works, after which the Polyak method is implemented. \u95 To find out if the Polyak method indeed converges faster than MSA. \u95 To find out if there exist alternative methods that are faster in convergence than the Polyak method and MSA. To find answers on these three topics the literature was studied. By reading the nature of the Polyak method is found out. When the Polyak method is understood, the method is implemented (and also MSA is implemented). That was needed for analysing the convergence of the methods. After the Polyak method is implemented research for alternative methods is done. Finally, all the described methods are compared and illustrations are given. In order to satisfy the increasing demand for more accurate model outcomes and to be able to compute the effects of different traffic policies, new and improved traffic assignment models are needed. While Static Traffic Assignment models may provide basic insights, only dynamic assignment models are able capture the true dynamic nature of traffic and therefore provide the analyst with more accurate forecast. An iterative process is needed to solve the Dynamic Traffic Assignment (DTA) model. This is because network conditions may change after performing network loading. At all the iterations, the path flows are updated by combining the results from the current iteration with the previous iteration. The 'classical' (e.g., derivative-based) fixed-point solution methods are often inappropriate for some problems. In such cases, the fixed-points are usually computed using one of the iterate averaging methods introduced by Robbins and Monro [3.1]. MSA, introduced by Sheffi and Powell [3.2], is probably the best known and most widely-used instance of iterate averaging methods. In iterate averaging methods estimates for the fixed-point are found. These estimates are called design points. MSA computes each new design point by adding a part of the observation evaluated in the previous design point with a part of the previous design point. MSA has the advantages of avoiding (potentially expensive) step size calculations, working directly with map outputs without requiring derivative calculations or other transformations, and being able to handle 'noisy' map evaluations (where the evaluation returns a value affected by a zero-mean disturbance). Other advantages of MSA are that it is simple to understand and that it is simple to implement. In many cases, however, the method's empirically observed convergence properties are disappointing: while it exhibits generally effective performance in the initial iterations, this is followed by a pronounced 'tail' effect, resulting in overall slow convergence. Approximately ten years ago, B.T. Polyak and J.A. Bather proposed two relatively minor modifications of iterate averaging methods which were rigorously shown to produce fixed-point estimates with asymptotically optimal properties. The Polyak method is a two-pass method. The first pass resembles MSA except that the step sizes are larger; this allows the algorithm to explore the solution space more aggressively but leads to greater variability in the outputs. The second pass is carried out offline (i.e., without influencing the first pass); it calculates an average of iterates that are generated by the first pass. The average calculated by the second pass at termination is the fixed-point solution estimate. A somewhat different approach was proposed by J.A. Bather. Here, the design point is derived from a combination of the average of previous design points with the average of previous evaluation results. Apart from the Polyak method and the Bather method, alternative methods are proposed. In total eight methods are applied and presented in this report. They were all compared with different stop criterions. MSA and the Polyak method were compared. To compare these methods and to compare also other methods a traffic problem with three cities and two routes is considered. This traffic problem is solved by using a DTA algorithm. For stopping this DTA algorithm there are different stop criteria. The stop criterion that is a combination of the route costs and flows is the best stop criterion for stopping the DTA algorithm. This stop criterion is reached after 190 iterations of MSA and after 226 iterations of the Polyak method. The conclusion is that MSA is faster in convergence than the Polyak method for this stop criterion. The Bather method, the Bliemer method and the Bliemer Moving method were compared. After 118 iterations of the Bather method, after 112 iterations of the Bliemer method and after 40 iterations of the Bliemer Moving method the stop criterion is reached. It can be concluded that the Bather method and the Bliemer method solve the problem in almost the same number of iterations. For different stop criterions the Bliemer Moving method much faster than is the four other methods. The Bliemer Moving method is the fastest method, but by combining two methods it's possible to get a method that is even faster in convergence than the methods shown before. Therefore, the MSA-Biiemer method, the MSA-Bather method and the Bliemer-Bather method are compared. The fastest method in convergence, for the stop criterion we chose, is the Bliemer-Bather method. The conclusion is that there are alternative methods that are much faster in convergence than the Method of Successive Averages and the Polyak method. The best alternative methods are the MSA-Bather method and the Bliemer-Bather method. The recommendation is to use the Bliemer-Bather method for solving Non Linear Programming (NLP) problems in transportation networks and to do further research how the values of the variables used in the Bliemer-Bather method have to be chosen.Transport & PlanningCivil Engineering and Geoscience
Economische aspecten van het agrarisch beheer in nationale landschapsparken
No full textOp grond van in bestaande literatuur geformuleerde gedachten wordt een idee gegeven hoe een Nationaal Landschapspark verwezenlijkt zou kunnen worden. Een van de belangrijkste problemen hierbij wordt gevormd door de economische problematiek van beheer in het agrarisch gebied, dat in de tot park aan te wijzen gebieden ligt. In deze scriptie zijn in een studiegebied de verschillende arbeidsinkomens in de agrarische sector bekeken bij verschillende landbouwkundige situaties. Als studiegebied is hiervoor genomen Midden-Brabant waarin het mogelijk Nationaal Landschapspark "Het Groene Hart van Brabant" is gelegen. Als meest relevante beperkingen zijn voor dit gebied aangenomen: beperking van dierlijke veredeling, beperking van de kunstmestgift, alsmede het instandhouden en restaureren van houtwallen en heggen in het landschap.Civil Engineering and Geoscience
The road pricing problem: A two-level optimisation approach
No full textLike in many countries congestion is also a big problem in the Netherlands. Congestion is damaging for the economy, the environment, and our own health. There is a big need for solving this problem. Possible solutions are: increasing road capacity, road pricing, fuel taxation, using other modes of transport, etc. Road pricing is seen as one of the best ways to decrease congestion. The traffic authorities want to influence travellers' behaviour with the help of road pricing causing them to change route, change departure time, change mode of travelling, etc., resulting in a more efficient use of transportation resources and relieving congestion on the roads. The road pricing problem can be seen as a situation where two decision makers have conflicting interests. The traffic authorities want to find an optimal toll pattern to achieve a situation in which the road system is in optimal form. I n other words, the traffic authorities want to minimise the total time travellers spend on the network. Travellers, on the other hand, want to minimise their own travel times. It is possible that there are travellers in the optimal network conditions, that can improve their own travel time. If they indeed choose another route for improving their travel time, network conditions deteriorate. The road pricing problem is restricted by some boundary conditions and basic assumptions of which 'the traffic assignment and the traffic flows will be static' is the most important. This research will focus on solving a mathematical representation of the road pricing problem using different methods taking into account the complexity and the properties of the road pricing model. Restrictions are made about the value of toll (lower and upper bound) and the number of roads that may be tolled (not all roads may/can be tolled). Therefore, the purpose of this thesis is not to toll every link, but to achieve a system state that is near the system optimum by charging toll on a subset of links and taking into account a lower and an upper bound for the tolls. This means that i t is a constrained optimisation problem and that some of the variables of the solution may be lying at the boundaries of their allowed ranges. The plan of approach for this research is to do first a literature study on bi-level optimisation and later on solution methods. The road pricing problem can be written mathematically as a two-level optimisation model (a special case of bi-level optimisation). The mathematical model consists of an upper and a lower level. The lower level is a representation of the behaviour of the travellers and in the upper level the traffic authorities are modelled. Both levels consist of an objective function and some constraints. The complexity and the convexity of the road pricing model are discussed. After that some solution methods and their convergence are discussed. Finally, these methods are tested on four networks and the results are reviewed. In complexity theory there are four classes of problems: P, NP, NP-complete, and NP-hard. Problems belonging to the class P are called (computationally) tractable. Problems that belong to the other three classes are called intractable problems. In the literature much is proved about the complexity of a bi-level problem. The conclusion is that the road pricing problem is NP-hard. This means that it is not likely to find an exact solution with a polynomial-time algorithm. Heuristics will therefore be used to solve the road pricing model. It is plausible that the road pricing model is a convex optimisation problem. In that case existing solution methods in order to find local optima can be used. Therefore, line search, first order gradient, direction set, and simplex methods are used to solve the road pricing model. These methods are implemented and eleven test cases are considered to assess and compare the methods. The seven best performing methods in the first four test cases are tested on the resulting test cases. These are the EDO/BS, the SD/BS, the Hestenes/Stiefel, the cyclic coordinate, the Rosenbrock, the Powell I I , and the complex method. They are compared per test case with respect to the value of the objective function (1), the number of the function evaluations (2), and the number of iterations (3). The methods are given notes from 1 t i l l 7 for these three criteria. The best performing method is given number 7 and the worst is given number 1. The average over all the test cases is shown in Table 6.1. Best performing means the smallest value of the objective function, the smallest number of function evaluations, or the smallest number of iterations. This is not the best way for comparing the methods, but it forms a notion of the performance of the methods compared with each other.Transport and PlanningCivil Engineering and Geoscience
Evaluating Multi-Class Model Predictive Control
No full textTraffic Control is a part of Dynamic Traffic Management where traffic management measures are controlled to optimize the capacity of networks. Since September 2011 Traffic Management Scenarios are applied to the A15 highway in the Port of Rotterdam Area. Traffic Management Scenarios are the most advanced Traffic Control methods that are applied in practice. The current state of art in Traffic Control is Model Predictive Control, an adaptive method that calculates the optimal control signal and adjusts it to changing traffic states. In this study this method is compared with the current implemented Traffic Management Scenarios for the A15 highway eastbound. Since this highway has a high share of freight traffic from the port, traffic is divided into two user-classes and a multi-class variant of Model Predictive Control will also be compared. The goal of this study is: To make a quantitative comparison based on economic costs among Traffic management Scenarios, Single-class Model Predictive Control and Multi-class Predictive Control. To be able to make this comparison a literature review is done on traffic control, including the two control methodologies that will be compared in this thesis, and multi-class traffic management measures. A categorization of control methodologies will be made to illustrate how Traffic Management Scenarios and model Predictive Control relate. Here will be shown that Traffic Management Scenarios are adaptable methods but that Model Predictive Control is even more adaptable. The traffic management measures that can be controlled by both control methods, ramp metering and route guidance, will also be described. Only route guidance is applied by the current Traffic Management Scenario Since the used Traffic Management Scenario was created based on experience and Model Predictive Control does not exist in practice yet there is described how both methods should be compared. First some requirements have to be set. These requirements are that the both methods should use the same network, control the same signals and that these control signals will be determined based on the same input data. To analyze the results of both methods, they should produce the same sort of output data. The easiest way to do this is performing a simulation experiment where both Traffic Management Scenario and Model Predictive Control use the same traffic model with a control module in it. The control module then can be replaced by either the Traffic Management Scenario, the Model Predictive Control or remain empty. BOS-HbR is a framework that fulfills these requirements and is therefore used for this study. It uses the A15 highway as its network. BOS-HbR consists of a estimation and prediction component. In the estimation component the input data retrieved from loop detectors is converted to a traffic state which serves as input for the prediction component. The prediction component uses multi-class model Fastlane to predict the traffic state and predict the results of the control method which will be inserted here. The Traffic Management Scenario used for the current study is the ‘A15 Haven Uit’ scenario developed by Regiodesk. For the current study a Traffic Management Scenario is created within BOS-HbR with the same (de)activation triggers as ‘A15 Haven Uit’. The Model Predictive Controller used in BOS-HbR will use the Matlab function fmincon as its optimization algorithm. The simulation experiment will be executed for three cases: a heavy peak hour, a regular peak hour and a severe accident. For each case a validation will be done to check if the model predictions for Fastlane matched reality. Also for each of these cases the experiments will be done with 5 demand levels - 90%, 95%, 100%, 105% and 110% of the original expected demand - to measure the robustness of the control methods. For the Traffic Management Scenario the conditions for the rerouting signal at Spijkenisse to be turned on will be described and there will be explained that road users will only comply with this signal if the off-ramp to the alternative route is congestion-free. The variables to be adjusted for the Model Predictive Controller are control interval, control horizon and prediction horizon. The results of these experiments are discussed basis of the following performance indicators: Total cost, average travel time per user class and robustness. In the cases of the heavy peak hour applying single-class Model Predictive Control shows double the improvement Traffic Scenarios achieved. In the regular peak hour this improvement was less and in the accident case the relative differences were minimal. In all cases single-class Model Predictive Control performs better than Traffic Management Scenarios, which shows a good improvement over the situation where no traffic control is applied. Multi-class Model Predictive Control has small improvements over single-class Model Predictive Control especially when looked at user-class specific travel times. The multi-class controller reroutes exclusively passenger car traffic and keeps the trucks on the main road. All control cases show an equal sensitivity to demand fluctuations. Overall it can be concluded that Model Predictive Control shows approximately the same improvement over Traffic Management Scenarios as the latter does over a situation where no traffic control is applied. Since Traffic Management Scenarios performed well in this study it is recommended to apply Traffic Management Scenarios with route guidance to more locations in the Netherlands where this is possible. It can also clear the road for a future implantation of Model Predictive Control. The Traffic Management Scenarios currently used are designed based on experience, it is interesting to see how Traffic Management Scenarios that are designed and optimized with a traffic model will perform. Rerouting the traffic multi-class showed good results for the Model Predictive Controller, therefore researching rerouting multi-class with a Traffic Management Scenario could also be interesting for the Port Area. Some interesting topics for further research following from this study are applying other traffic management measures except rerouting in the Port area and a behavioral research on how traffic responds to the DRIP signals that guide it, because in this research assumptions on compliance to these signals were made.Transport & PlanningTransport & PlanningCivil Engineering and Geoscience
Multi-cohort Machine Learning Identifies Predictors of Cognitive Impairment in Parkinson's Disease
No full textpeer reviewedCognitive impairment is a frequent complication of Parkinson’s disease (PD), affecting up to half of newly diagnosed patients. To improve early detection and risk assessment, we developed machine learning models using clinical data from three independent PD cohorts, which are (LuxPARK, PPMI, ICEBERG) Models were trained to predict mild cognitive impairment (PD-MCI) and subjective cognitive decline (SCD) using Explainable Artificial Intelligence (XAI) for classification and time-to-event analysis. Multi-cohort models showed greater performance stability over single-cohort models, while retaining competitive average performance. Age at diagnosis and visuospatial ability were identified as key predictors. Significant sex differences observed highlight the importance of considering sex-specific factors in cognitive assessment. Men were more likely to report SCD. Our findings highlight the potential of multi-cohort machine learning for early identification and personalized management of cognitive decline in PD.U-AGR-7200 - INTER/22/17104370/RECAST - GLAAB Enrico3. Good health and well-bein
Computational systems biology methods for cross-disease comparison of omics data
No full textpeer reviewedComplex diseases often share genetic susceptibility factors, molecular pathways, and pathological mechanisms. Understanding these commonalities through systematic cross-disease comparisons can reveal both disease-specific and shared biomarkers, potentially suggesting new therapeutic targets and opportunities for drug repurposing.
In recent years, the growth of multi-omics datasets across diverse diseases, coupled with advances in computational systems biology, has enabled sophisticated cross-disease analyses. New methodological frameworks have emerged for integrating and comparing disease-specific molecular signatures, from gene-level analyses to complex network-based approaches.
Here, we present a comprehensive framework for computational cross-disease comparison and integration of omics data, systematically covering established and emerging methodologies. These include gene-level comparative analyses, pathway-based approaches, network biology methods, matrix factorization techniques, and machine learning approaches. We examine important aspects of data preprocessing, normalization, and integration, suggesting practical solutions to common technical challenges. We provide a detailed overview of relevant software tools and databases, discussing their strengths, limitations, and optimal use cases for cross-disease analysis. Finally, we explore current trends in cross-disease omics analysis, particularly through deep learning methods, highlighting new opportunities for methodological innovation and biological discovery in this field.
This compilation of computational methods and practical insights aims to serve as a resource both for bioinformaticians seeking guidance on optimal method selection and biomedical researchers interested in applied cross-disease analyses. In addition to highlighting practical recommendations and common pitfalls, it provides an entry point to the extensive literature in the field, supporting readers in identifying and further exploring suitable methods for their research needs.U-AGR-7200 - INTER/22/17104370/RECAST - GLAAB Enrico3. Good health and well-bein
Levodopa-induced dyskinesia in Parkinson's disease: Insights from cross-cohort prognostic analysis using machine learning
No full textpeer reviewedBackground
Prolonged levodopa treatment in Parkinson's disease (PD) often leads to motor complications, including levodopa-induced dyskinesia (LID). Despite continuous levodopa treatment, some patients do not develop LID symptoms, even in later stages of the disease.
Objective
This study explores machine learning (ML) methods using baseline clinical characteristics to predict the development of LID in PD patients over four years, across multiple cohorts.
Methods
Using interpretable ML approaches, we analyzed clinical data from three independent longitudinal PD cohorts (LuxPARK, n = 356; PPMI, n = 484; ICEBERG, n = 113) to develop cross-cohort prognostic models and identify potential predictors for the development of LID. We examined cohort-specific and shared predictive factors, assessing model performance and stability through cross-validation analyses.
Results
Consistent cross-validation results for single and multiple cohort analyses highlighted the effectiveness of the ML models and identified baseline clinical characteristics with significant predictive value for the LID prognosis in PD. Predictors positively correlated with LID include axial symptoms, freezing of gait, and rigidity in the lower extremities. Conversely, the risk of developing LID was inversely associated with the occurrence of resting tremors, higher body weight, later onset of PD, and visuospatial abilities.
Conclusions
This study presents interpretable ML models for dyskinesia prognosis with significant predictive power in cross-cohort analyses. The models may pave the way for proactive interventions against dyskinesia in PD by optimizing levodopa dosing regimens and adjunct treatments with dopamine agonists or MAO-B inhibitors, and by employing non-pharmacological interventions such as dietary adjustments affecting levodopa absorption for high-risk LID patients.U-AGR-7200 - INTER/22/17104370/RECAST - GLAAB Enrico3. Good health and well-bein
Interpretable Machine Learning for Cross-Cohort Prediction of Motor Fluctuations in Parkinson s Disease
No full textpeer reviewedBackground:
Motor fluctuations are a common complication in later stages of Parkinson's disease (PD) and significantly affect patients' quality of life. Robustly identifying risk and protective factors for this complication across distinct cohorts could lead to improved disease management.
Objectives:
To identify key prognostic factors for motor fluctuations in PD by using machine learning and exploring their associations in the context of the prior literature.
Methods:
We applied interpretable machine learning techniques for time-to-event analysis and prediction of motor fluctuations within four years in three longitudinal PD cohorts. Prognostic models were cross-validated to identify robust predictors, and the performance, stability, calibration, and utility for clinical decision-making were assessed.
Results:
Cross-validation analyses suggest the effectiveness of the models in identifying significant baseline predictors. MDS-UPDRS Parts I and II, freezing of gait, axial symptoms, rigidity, and pathogenic GBA and LRRK2 variants were positively correlated with motor fluctuations. Conversely, motor fluctuations were inversely associated with tremors and late age of onset of PD. Cross-cohort data integration provides more stable predictions, reducing cohort-specific bias and enhancing robustness. Decision curve and calibration analysis confirms the models’ practical utility and alignment of predictions with observed outcomes.
Conclusions:
Interpretable machine learning models can effectively predict motor fluctuations in PD from baseline clinical data. Cross-cohort data integration increases the stability of selected predictors. Calibration and decision curve analyses confirm the model’s reliability and utility for practical clinical applications.U-AGR-7200 - INTER/22/17104370/RECAST - GLAAB Enrico3. Good health and well-bein
Comprehensive blood metabolomics profiling of Parkinson's disease reveals coordinated alterations in xanthine metabolism
No full textpeer reviewedParkinson's disease (PD) is a highly heterogeneous disorder with several environmental and genetic factors contributing to the disease initiation and progression. Effective disease-modifying therapies and robust biomarker signatures for the early pre-motor and motor stages of the disease are still lacking, and an improved understanding of the molecular changes characterizing PD could help to reveal new diagnostic and prognostic markers and possible targets for the study of pharmaceutical interventions.
Here, we report results from a cohort-wide blood plasma metabolic profiling of PD patients and controls in the Luxembourg Parkinson’s Study to detect disease-associated alterations at the level of systemic cellular process and network alterations. We identified statistically significant changes in both individual metabolite levels and global pathway activities in PD vs. controls and significant correlations with motor impairment scores. As a primary observation when investigating shared molecular sub-network alterations, we detect pronounced and coordinated increased metabolite abundances in xanthine metabolism in de novo patients, which are consistent with previous PD case/control transcriptomics data from an independent cohort in terms of known enzyme-metabolite network relationships. From the integrated metabolomics
and transcriptomics network analysis, the enzyme hypoxanthine phosphoribosyltransferase 1 (HPRT1) is determined as a potential key regulator controlling the shared changes in xanthine metabolism and linking them to a mechanism that may contribute to pathological loss of cellular adenosine triphosphate (ATP) in PD.
Overall, the investigations revealed significant PD-associated metabolome alterations,
including pronounced changes in xanthine metabolism that are mechanistically congruent with alterations observed in independent transcriptomics data. The enzyme HPRT1 may merit further investigation as a main regulator of these network alterations and as a potential therapeutic target to address downstream molecular pathology in PD.Validating Digital Biomarkers For Better Personalized Treatment Of Parkinson’s Disease3. Good health and well-bein
An observational study of a magneto-acoustic wave in the solar corona
No full textThe Solar Eclipse Corona Imaging System (SECIS) observed a strong 6-s oscillation in an active region coronal loop, during the 1999 August 11 total solar eclipse. In the present paper we show that this oscillation is associated with a fast-mode magneto-acoustic wave that travels through the loop apex with a velocity of 2100 km s−1. We use near-simultaneous SOHO observations to calculate the parameters of the loop and its surroundings such as density, temperature and their spatial variation. We find that the temporal evolution of the intensity is in agreement with the model of an impulsively generated, fast-mode wave
