In order to improve parameter identifiability in mathematical tumor models, we propose a Bayesian framework using Markov Chain Monte Carlo (MCMC) methods to fit pharmacodynamic parameters to in vitro tumor spheroid data. We conducted cytotoxicity experiments on Brca1-deficient murine mammary tumor cells, measured tumor volume changes via time-lapse fluorescence microscopy, and calibrated a modified tumor growth model to the resulting data using the No-U-Turn Sampler (NUTS) algorithm. Our results demonstrate that MCMC-based inference yields biologically meaningful posterior distributions, even under data uncertainty. We observe trends in parameter estimates across multiple drug concentrations and identify cases where parameter identifiability is limited. This approach offers a robust framework for model calibration and uncertainty quantification, which supports future efforts in therapy optimization based on in vitro experiments.

Embodiment is a key aspect of human intelligence, related to our ability of to identify the context of the individual experiences at a given time, corresponding to the natural constraints represented by our body. Learning from higher cognitive functions and social coordination between humans can support building intelligent robot systems and facilitates harmonious human-machine interactions. This position paper provides an overview of neural structures and neural dynamics contributing to human cognitive functions, including multisensory integration, Gestalt formation, perception, and building sensory associations. Embodied cognitive principles are illustrated through the intentional action-perception cycle. The results are applied to the design novel algorithms for brain-inspired cognitive robotics. Example scenarios include imitation learning, and the emergence of dialogue patterns in social robotics settings.

Several prior studies focused on the estimation of the probability of Type 2 Diabetes Mellitus (T2DM) complications. None of the references used sophisticated mathematical models to simulate the long-term progression of the disease when estimating the probability of a complication. This study extends an existing T2DM model to predict the probability of Diabetic Retinopathy. The benefit of this approach is that we will need much less information to predict the probability of a complication than in a standalone model, as the progression of diabetes will be estimated with an identifiable model. To expand the model, a detailed qualitative analysis was required first to write the model equation along with relationships that can be supported by the pathogenesis of diabetes and the biological background of the development of complications. Then the collection of data necessary for setting up the model, which in the course of our present work was implemented by quantitative analysis of publicly available data releases. The next step was to write the model and identify it using the available data. Finally, we validated the model using a data set from a data source independent of the previous ones and evaluated the results. In the course of our present work, we focused on the probability of developing Diabetic Retinopathy. The completed model is suitable for supplementing the simulation of the initial model in such a way as to estimate the probability of retinopathy appearing in a patient during the progression of the disease.

The future of healthcare increasingly depends on personalized treatments based on accurate modeling of patient-specific tumor dynamics. Tumor growth models play a key role in quantifying responses to chemotherapy. In preclinical studies, data collection is often constrained by ethical and practical limitations, resulting in sparse and heterogeneous measurements. Consequently, it is essential to extract the maximum possible information from the available data by focusing on model-consistent segments and minimizing the impact of measurement noise or biological variability that the model cannot capture. In this study, we estimate the parameters of an in vivo tumor model using nonlinear mixed-effects (NLME) modeling. A major challenge is that the mathematical model cannot describe resistant tumor phases, and NLME assumes inter-individual similarity, which may not hold in heterogeneous populations. Additionally, NLME fitting is sensitive to initial values, complicating the distinction between poor fit and poor initialization. In order to address these issues, we developed a multi-stage estimation workflow. We begin with a global NLME fit, followed by automatic exclusion of resistant segments and re-estimation on the trimmed data. Least squares fits are used to classify individuals into well- and poorly-fitting subgroups. Each group is then refitted using NLME with feedback-based parameter refinement. This approach improves estimation robustness and enables model-driven stratification that may reflect underlying biological heterogeneity.

Diabetes mellitus is a chronic metabolic disorder requiring meticulous blood glucose regulation to minimize both acute complications and long-term vascular damage. Traditional glucose monitoring approaches—such as finger-prick tests and continuous glucose monitoring (CGM)—primarily support reactive interventions, often falling short in enabling proactive management. This study proposes a deep learning-based predictive framework for blood glucose level estimation using historical CGM data. The model’s performance was evaluated using standard metrics including Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R2) score. Experimental results across multiple patients reveal that the model achieved RMSE values ranging from 19.37 to 28.57, and MAPE values between 7.76 and 13.31. The highest predictive accuracy was observed for Patient 570 (RMSE: 20.38, MAPE: 7.76), while the model struggled with higher variability in Patient 559. These findings demonstrate the model’s potential in delivering personalized, anticipatory glycemic control, thereby supporting more effective diabetes management strategies.

This paper introduces an integrated Hardware-In-the-Loop (HIL) testing framework, combining the UVA/Padova Type 1 Diabetes Simulator with AndroidAPS, an open-source artificial pancreas system. This integration forms a testing environment capable of evaluating insulin regulation algorithms under both virtual and real hardware conditions. The FDA-approved UVA / Padova Simulator models glucose-insulin dynamics and meal digestion. Paired with AndroidAPS, the system can actuate real-world insulin pumps to test insulin delivery control algorithms. The framework is tied together by various REST APIs and uses the Flask framework for efficient data exchange and system connectivity. The HIL approach provides a robust platform for functional and reliability testing of these algorithms. The developed APIs are open-source: https://github.com/OE-Diab/AP-HIL

Dynamically emerging application of highly automated systems in many areas of our life increasingly needs task specific advanced model-simulation representations, model-based communications, cyber-physical controls, learning algorithms, and other engineering related solution elements in highly integrated environments where communication is established between model representations. In this way, model driving, and control are fully integrated in the innovation cycle from the idea to product support. This paper proposes and introduces a novel method for this integration aiming at a solution capable of theory, methodology, practice, and experience representations in the same model. The method follows the idea of three paradigm innovation where theoretical and experimental research and development are controlled by executable reactive model. Highly integrated, reactive, autonomous, and system representation eligible model is supposed and supported. Implementation of the proposed method applies high level modeling, realistic simulation, communication, and collaboration platform to provide theoretically grounded, methodologically up to date, and industrially eligible models for wide thematical area including integrated medical engineering solutions. Reactive and autonomous model requires intelligent computing and knowledge engineering intensive solutions for innovations in increasingly autonomous industrial and commercial products and other structures. Because communications in the proposed integration are established between model entities, natural language processing is required to learn written and spoken human inputs for model communication. Main issues in this paper are scenario of the software integration, elements and structure of the proposed solution, parameter relational features, collaborative data management, relevant machine learning models, impact, and implementation.

平行交通是一种实现智能交通管理与控制的综合性范式,致力于解决人类行为和社会因素的复杂性问题。近年来,基础模型(foundational models, FMs)的崛起为平行交通的实现提供了新的可能。但这种模型固有的知识陈旧、“幻觉”现象以及“黑盒”特性削弱了其决策的可靠性和可信度。为解决这一问题,提出一种基于检索增强生成与思维链提示(chain-of-thought prompting)的平行交通框架TransRAG。该框架由紧密协作的存储层、管理层和执行层组成,旨在为用户提供个性且多样化的交通服务。其中,存储层引入的外部知识增强了管理层中基础模型的性能,以实现复杂的计算实验。执行层中人工交通系统与实际交通系统的虚实交互使得管理层的决策得到持续优化,从而实现动态知识更新和灵活的策略调整,以适应不断变化的交通环境。此外,TransRAG通过区块链、智能合约和缓存技术的集成,能够有效应对单点故障、隐私泄露以及数据访问延迟等问题,从而加速推进向“6S”交通5.0的全面迈进。

In this paper, a novel approach for detecting anomalies in the non-linear fully-submerged hydrofoil attitude control system (HACS) is proposed, even in the presence of time-varying disturbances. To address the trade-off between robustness against disturbances and optimality in terms of estimation error, the extended sliding innovation filter (SIF) is employed as a state estimator for the target non-linear HACS. By utilizing a switching gain with a sliding boundary layer, the SIF inherently possesses a degree of robustness to estimation issues that may involve fault conditions or factors of disturbances. A residual framework is subsequently established to achieve state tracking and comparison. The residual evaluation for the fault detection (FD) scheme is then easily conducted using statistical methods such as the modified Z-Score and the peak signal-to-noise ratio (PSNR). Finally, the effectiveness of the developed FD strategy is substantiated through experiments conducted on a hardware-in-loop (HIL) platform. Comparative analysis with state-of-the-art robust UKF algorithms reveals the impressive fault detection proficiency of the proposed strategy. Note to Practitioners—This paper was motivated by the challenges of state estimation and FD for hydrofoil crafts under stochastic ocean wave disturbances. The extended SIF-based approach enhances robustness in the estimation and FD against disturbances by introducing a dynamic layer. Moreover, the adaptive layer indicates system anomalies as significant changes, which can assist engineers in promptly identifying anomalies. Furthermore, the modified statistics used in the FD scheme effectively reduce interference in the results. The developed FD method is easy to implement without linearizing the non-linear target system. The experimental validation conducted on the dSPACE platform utilizing the PCH-1 model illustrates the practicality of the proposed strategy for pertinent practitioners.

The fuzzy-possibilistic product partition c-means (FPPPCM) algorithm was proposed as a robust solution to the c-means clustering problem, in which outlier data behave similarly to distant objects in gravity systems. Although FPP-PCM reliably provides fine partitions when its parameters are well chosen, things can be difficult when it is not initialized properly. To avoid such cases, this paper proposes a self-tuning version of the FPPPCM algorithm, which incorporates some cluster size controlling variables into the objective function that allow for the adjustment of the so-called possibilistic penalty terms during the alternative optimization process. The proposed method was evaluated using four standard test datasets in three different scenarios: (1) no added noise; (2) a single outlier added; (3) multiple noisy items added. The partitions provided by the proposed algorithm were evaluated based on cluster purity, normalized mutual information and adjusted Rand index, and was compared with the outcome of previous clustering models. The proposed method performed better or at least at the same quality level at previous ones, while reducing the number of parameters the user is responsible for.

This paper is about a special configuration of engineering model and modeling platform for model development that serves widely connected research in applied informatics considering demands by highly automated achievements. Initiative for this special application of engineering modeling platform was motivated by engineering purposed software development towards reactive, autonomous, and widely contextual solutions to accommodate tasks in extending variety of integrated issues. After characterizing the multiphysical way of multidisciplinary model powered engineering research, this paper introduces novel model mediated scenario in which research specific engineering software related activities are placed with the emphasis on flexible model and software configuration considering composition of complex multidisciplinary solutions for multiphysical engineering representations and simulations those are extended to human organ and other bioinspired issues. Additional contributions in this paper are about capabilities of reactive autonomous models, extended representation of behaviors, and novel experimental model for research in applied informatics. Finally, application of the above experimental model in research using scientifically renewable world level engineering modeling platform at the Virtual Research Laboratory, and evaluation and advantages of the proposed research are introduced.

Chemotherapy optimization based on mathematical models is a promising direction of personalized medicine. Personalizing, thus optimizing treatments, may have multiple advantages, from fewer side effects to lower costs. However, personalization is a complicated process in practice. We discuss a mathematical model of tumor growth and therapy optimization algorithms that can be used to personalize therapies. The therapy generation is based on the concept of keeping the drug level over a specified value. A mixed-effect model is used for parametric identification, and the doses are calculated using a two-compartment model for drug pharmacokinetics, and a nonlinear pharmacodynamics and tumor dynamics model. We propose personalized therapy generation algorithms for having a maximal effect and minimal effective doses. We handle inter-and intra-patient variability for the minimal effective dose therapy. Results from mouse experiments for the personalized therapy are discussed and the algorithms are compared to a generic protocol based on overall survival. The experimental results show that the introduced algorithms significantly increased the overall survival of the mice, demonstrating that by control engineering methods an efficient modality of cancer therapy may be possible.

Automated brain tumor classification is an intensively investigated problem, which recently attracted significant attention. Convolutional neural networks (CNN) and deep learning represent the standard for the foundation of any recent solution. This paper proposes two simplified VGG architectures and investigates their capabilities and limitations, in comparison with state-of-the-art CNN networks deployed via transfer learning. Various parameter settings are involved in the evaluation process, including different kernel sizes, dropout rules, loss functions, etc. Networks are trained and tested on a public brain tumor classification data set consisting of 3064 images and three tumor classes (meningioma, glioma and pituitary tumor). The thorough evaluation process revealed that the proposed CNN models can achieve competitive performances with regard to state-of-the-art methods in several scenarios. The best achieved accuracy benchmarks are 98.2% overall Dice similarity score and correct decision rate, and AUC values over 99.6% for each of the three tumor classes.

Reinforcement learning (RL) has shown promise in controlling blood glucose levels in a personalized way in type 1 diabetic patients. In this study, we investigate the impact of different activation functions and layer numbers on RL performance in blood glucose control. We train RL agents with various combinations of activation functions and layer numbers on a virtual patient model. The RL agents are evaluated based on their ability to maintain blood glucose levels within a target range while minimizing the frequency and magnitude of hypoglycemia and hyperglycemia events. Our results show that the choice of activation function and layer number significantly affects the RL performance. Specifically, the agents with ReLU activation functions and two or three hidden layers outperform the other agents, achieving a higher percentage of time in the target range and fewer hypoglycemia and hyperglycemia events. These findings provide valuable insights for the development of RL-based blood glucose control systems in type 1 diabetic patients.

Brain tumor segmentation has been a widely researched topic for decades, and it intensified ten years ago as a consequence of the Brain Tumor Segmentation Challenges (BraTS), which provided and yearly updated a standard multi-spectral brain tumor MRI data set and a unified evaluation framework to the research community. This paper proposes a procedure for brain tumor segmentation, which uses a spatial histogram enhancement method to preprocess the data, and two identical cascaded U-net networks that work with 3D convolution. The first U-net accomplishes an intermediary segmentation of the brain volume, while the second one reevaluates the labels given to pixels based on the labels of neighbor pixels. The output of both U-nets are evaluated using statistical accuracy benchmarks. The proposed procedure achieved an average Dice score of 88.8% on the high-grade glioma records of the BraTS 2019 training data set. Post-processing increased the average Dice score by 1.1%, but in case of typical small high-grade tumor lesion it can achieve an improvement of up to 5%.

Despite the increasing availability and reliability of artificial pancreas devices, many people with type 1 diabetes mellitus are still on multiple daily injections therapy consisting of a daily basal insulin injection and mealtime boluses. The use of an insulin bolus advisor may improve glycaemic control as well as reduce the burden of the disease on these patients. This paper investigates the application of an impulsive model predictive controller in a bolus advisor system. The bolus calculator is assessed in closed-loop simulations using different basal insulin scenarios. The simulations show that a model predictive controller can achieve good glycaemic control and may be suitable to give bolus recommendations to people with diabetes. Higher basal insulin levels can result in better times in the target range but also carry a higher risk of hypoglycemia.

Like many physiological systems, the various mathematical models describing the glucose-insulin system can only have non-negative inputs. Thus, the control method — often model predictive control in artificial pancreas systems—must provide a non-negative control signal. Existing solutions include saturation and constrained optimization. In this paper, we propose a dynamic extension of the patient model as a way of ensuring the positivity of the control signal, a method previously applied in tumor growth control. We evaluate the controller in a closed-loop simulation and compare the results with a controller using saturation. Our simulations show that control performance with the extended model can reach or exceed the performance achieved with saturation.

Background:Human workers are indispensable in the human–cyber-physical system in the forthcoming Industry 5.0. As inappropriate work content induces stress and harmful effects on human performance, engineering applications search for a physiological indicator for monitoring the well-being state of workers during work; thus, the work content can be modified accordingly. The primary aim of this study is to assess whether heart rate variability (HRV) can be a valid and reliable indicator of acute work-content-related stress (AWCRS) in real time during industrial work. Second, we aim to provide a broader scope of HRV usage as a stress indicator in this context.Methods:A search was conducted in Scopus, IEEE Xplore, PubMed, and Web of Science between 1 January 2000 and 1 June 2022. Eligible articles are analyzed regarding study design, population, assessment of AWCRS, and its association with HRV.Results:A total of 14 studies met the inclusion criteria. No randomized control trial (RCT) was conducted to assess the association between AWCRS and HRV. Five observational studies were performed. Both AWCRS and HRV were measured in nine further studies, but their associations were not analyzed. Results suggest that HRV does not fully reflect the AWCRS during work, and it is problematic to measure the effect of AWCRS on HRV in the real manufacturing environment. The evidence is insufficient for a reliable conclusion about the HRV diagnostic role as an indicator of human worker status.Conclusion:This review is valuable in the Operator 4.0 paradigm, calling for more trials to validate the use of HRV to measure AWCRS on human workers.

One of the most challenging area of diabetes research is to provide such automated insulin delivery systems – so called artificial pancreas systems – that have robust and adaptive capabilities in a highly sophysticated way. I.e. they are able to provide robust insulin delivery actions at the beginning of the therapy to satisfy the requirements of the patients without knowing the users daily lifestyle and preferences however adaptive on the short-term to learn these patient specifics to increase the quality of the therapy. One possible solution is the closed-loop systems that have self-learning features. In the present study, we have examined a glucose regulatory problem using direct reinforcement learning based controller. The approach represents the fully automatic insulin administration as the timepoint and the carbohydrate content of the meals were unknown and randomized. We constructed a virtual environment of the patient with type 1 diabetes by applying a mathematical model. Proximal policy optimization learning model with continuous action space was used. Furthermore, we evaluated the effect of different training lengths on the test scenario.

The effects of individual physiological phenomena play an important role considering the accuracy of artificial pancreas systems. An example of these phenomena is the heart rate which is easy to measure. There is a connection between heart rate and endogenous glucose production that significantly influences the blood glucose level. The proper implementation of heart rate could lead to defining physical activity in TIDM models. The aim of the current study is to examine how the change in endogenous glucose production influences the fitting accuracies in model versions with different complexity. Our extensions include a heart rate dependent endogenous glucose production equation, modeling the effect of physical activity, and a part defining the effect of insulin on endogenous glucose production. The joint effect of the mentioned extensions was also considered.

This paper is a contribution to concepts and methodologies of context definition in Engineering Model System (EMS). Change for contextual modeling was important paradigm shift in engineering at beginning of this new century. By now, EMS is a very complex model because it is required to serve all engineering activities during lifecycle of an engineering achievement (EA). In an EMS, the structure of contexts serves integration as glue. An EA is the objective of an engineering program and generally is an industrial or commercial product. Four new concepts and methods were introduced during the work for this paper with the requirement of their integration in EMS namely the Integrated Lifecycle Engineering (ILE), the Integrated Autonomous Model System (IAMS), the Reactive Context Structure (RCS), and the Integrated Research Project (IRP). ILE organizes research intensive engineering activities to support connection between essential engineering and relevant modeling software platform (MPS) activities. ILE includes fundamental and problem solving research in new context. IAMS is based on formerly published EMS concepts and includes new specifics. In the center of IAMS, enhanced structure of context definitions and their organization constitute glue in the demanded complex model. IAMS is characterized as an enhanced media for engineering communication. RCS serves as integrated unit of IAMS to enhance context driven reactive communication. In this way, IAMS includes two ways reactive context driven connection between IAMS and the physically operated Cyber Physical Biological System (CPBS) it represents. IAMS serves as digital twin of CPBS. The above new concepts and methods are suitable to develop as user defined components in EMS using MPS resources. IRP is introduced as a specific integrated application of ILE, IAMS, and RCS, concentrating on PhD student research specifics.

Engineering software platform supports engineering activities in continuously widening disciplinary area of complex systems operated industrial and commercial products and other engineering achievements. Engineering activities increasingly include research extending the conventional product lifecycle engineering to the whole innovation cycle. Comprehensive engineering platform comprises wide range of integrated software solutions to manage complex model systems to represent situation controlled autonomous products and offers all software solutions necessary for the integrated innovation and life cycle. By now, engineering platforms are amongst the largest and most complex applications of advanced software. This paper reports recent contributions to concept and methodology of research integration representations for engineering model systems using research capabilities of engineering platform. First, concept on model organized research project (MORP) is introduced. MORP is a new model system based concept of research project which is managed using capabilities of software organized in these platforms. MORP relies on the formerly defined concept of model mediated research (MMR) which is extended to research in situation control of autonomous functions of the represented systems operated engineering achievements recognizing that situation control reorganizes engineering related research to a great extent. Following this, connection of MORP with software which provides situation based control for physical execution in cyber physical system (CPS) is analyzed and discussed. The MMR based MORP is about pilot projecting at the recently established virtual research laboratory (VRL) at the Doctoral School of Applied Informatics and Applied Mathematics (DSAIAM) at the Óbuda University.

Abstract This paper proposes an improved brain tumor segmentation method based on visual saliency features on MRI image volumes. The proposed method introduces a novel combination of multiple MRI modalities used as pseudo-color channels for highlighting the potential tumors. The novel pseudo-color model incorporates healthy templates generated from the MRI slices without tumors. The constructed healthy templates are also used during the training of neural network models. Based on a saliency map built using the pseudo-color templates, combination models are proposed, fusing the saliency map with convolutional neural networks’ prediction maps to improve predictions and to reduce the networks’ eventual overfitting which may result in weaker predictions for previously unseen cases. By introducing the combination technique for deep learning techniques and saliency-based, handcrafted feature models, the fusion approach shows good abstraction capabilities and it is able to handle diverse cases that the networks were less trained for. The proposed methods were tested on the BRATS2015 and BRATS2018 databases, and the quantitative results show that hybrid models (including both trained and handcrafted features) can be promising alternatives for reaching higher segmentation performance. Moreover, healthy templates can provide additional information for the training process, enhancing the prediction performance of neural network models.

The automatic segmentation of medical images represents a research domain of high interest. This paper proposes an automatic procedure for the detection and segmentation of gliomas from multi-spectral MRI data. The procedure is based on a machine learning approach: it uses ensembles of binary decision trees trained to distinguish pixels belonging to gliomas to those that represent normal tissues. The classification employs 100 computed features beside the four observed ones, including morphological, gradients and Gabor wavelet features. The output of the decision ensemble is fed to morphological and structural post-processing, which regularize the shape of the detected tumors and improve the segmentation quality. The proposed procedure was evaluated using the BraTS 2015 train data, both the high-grade (HG) and the low-grade (LG) glioma records. The highest overall Dice scores achieved were 86.5% for HG and 84.6% for LG glioma volumes.

In this paper we propose an end-to-end, automatic, online camera-LIDAR calibration approach, for application in self driving vehicle navigation. The main idea is to connect the image domain and the 3D space by generating point clouds from camera data while driving, using a structure from motion (SfM) pipeline, and use it as the basis for registration. As a core step of the algorithm we introduce an object level alignment to transform the generated and captured point clouds into a common coordinate system. Finally, we calculate the correspondences between the 2D image domain and the 3D LIDAR point clouds, to produce the registration. We evaluated the method in various different real life traffic scenarios.

Mathematical modeling of tumor growth dynamics may have great impact on modern medicine. The dynamical model of tumor growth which describes the effect of drugs can be used e.g., for therapy optimization, therapy supervision, drug development. Based on a single drug tumor growth model, we create a model that can be used to describe the effect of two drugs. The extended model is created using formal reaction kinetics analogy, thus the meaning of the equations is interpretable for experts not familiar with differential equations. We carry out parametric identification using nonlinear mixed-effect model, for the identification we use measurements from mice experiments carried out using bevacizumab and fluorouracil treatment. The results of the identification show that the measurements can be reproduced using the model with small error, and the interpatient variability for most of the parameters is relatively low.

The steadily growing amount of medical image data requires automatic segmentation algorithms and decision support, because at a certain time, there will not be enough human experts to establish the diagnosis for every patient. It would be a good question to establish whether this day has already arrived or not. Computerized screening and diagnosis of brain tumor is an intensively investigated domain, especially since the first Brain Tumor Segmentation Challenge (BraTS) organized seven years ago. Several ensemble learning solutions have been proposed lately to the brain tumor segmentation problem. This paper presents an evaluation framework designed to test the accuracy and efficiency of ensemble learning algorithms deployed for brain tumor segmentation using the BraTS 2016 train data set. Within this category of machine learning algorithms, random forest was found the most appropriate, both in terms of precision and runtime.

In case of physiological systems state and parameter estimation is a crucial question. It is key to describe given patient population with appropriate accuracy. Furthermore, state feedback kind of applications also require some sort of estimation procedure in order to get internal information about the controlled system. Linear parameter varying (LPV) framework is beneficial for controller design as well. However, to realize the necessary scheduling parameters, estimation of both state variables and model parameters is needed. A possible solution is the application of Dual Extended Kalman Filter (DEKF) which is able to estimate these signals. The developed framework can be used to design LPV based controller in our further work. In this study we introduce our developed DEKF solution by using the widely applied Cambridge Type 1 Diabetes Mellitus (TIDM) model for virtual patient generation. We have found that our solution is able to estimate the state variables with good accuracy. The variation of parameters can also be tracked by using the proposed solution.

In natural sciences, especially in life sciences, controller designers frequently meet the problem that though the controlled system is modeled by a set of nonlinearly coupled Ordinary Differential Equations (ODE) containing various independent variables, only a single control input is available by the use of which the propagation of only one variable has to be controlled. Normally the controlled state variable can be observed by direct measurements, while no sensors are available for obtaining information on the propagation of the other ones. Though in the possession of a reliable system model one has good odds to develop state observers, in the practice just the reliable model used to be missing. While the traditional control design methodologies normally need some state estimation, the Fixed Point Iteration-based (FPI) Adaptive Controller was developed to evade this difficulty. In this design instead modeling the effects of the propagation of the various state variables, these effects are directly observed and compensated on this basis. This approach can be used without structural modification if certain effects appear through some time-delay (Delayed Differential Equations - DDE). In many cases simple and effective models can be developed that contain only pure delay effects. In this paper it is shown that a recent DDE model of Diabetes Mellitus can be used in the FPI-based adaptive blood glucose concentration level control even if the available model is very imprecise. This statement is substantiated by numerical simulations.

The advantages of using advanced control techniques related to physiological applications are unquestionable as it was proven in many cases in the recent times. Although, there are several challenges that practitioners need to face. For example, the lack of precise information about the internal state of the patients, i.e. the inter-and intra-patient variabilities which cause uncertainties that need to be tolerated by the applied controllers. In this study an alternative solution is presented for control of tumor growth. Uncertainties and nonlinearities are handled by the applied Linear Parameter Varying (LPV) methodology completed by Tensor Product (TP) model transformation. Linear Matrix Inequalities (LMI) based optimization are used for controller design. The lack of information about the internal state is solved by using Extended Kalman Filter (EKF) to estimate the non-measurable state variables. The developed control structure is able to enforce the controlled system to behave as a predefined reference system. We show that the control framework operates well and reaches the determined aims of the control.

The utilization of modern and advanced control engineering related methods for the control, estimation and assessment of physiological applications is widespread. It is also well-known that this engineering apparatus is executed on digital computers. The current insufficiency of available and accurate discretized models, especially in case of Diabetes Mellitus (DM), provides incentive for this research. The researchers typically approximate the continuous solutions which may not be the best alternative in many cases, in particular considering numerical stability and cost-effectiveness. In this paper we performed an analysis of the available discretization options in order to develop discrete models with a special focus on the Linear Parameter Varying (LPV) systems. LPV techniques are very useful frameworks which allow the application of linear controller, observer and estimator design. In this study, three LPV discretization and two Jacobian based discretization methods are introduced and analyzed to provide a basis for our further investigations in the topic.

In this paper we examine how can be combined the Linear Parameter Varying (LPV) modeling technique with the Linear Matrix Inequality (LMI) based controller and observer design methodology in order to control the tumor growth via anti-angiogenic inhibition. We introduce the important physiological knowledge regard to the control problem together with the design procedure. We used a recently developed minimal model which describes the tumor growth dynamics beside anti-angiogenic inhibition and we transformed this model into the difference based qLPV model. After, LMI based controller and observer were designed by pole clustering LMIs and we realized the control structure. Our aim was to develop a control environment which is - however - advanced, but it can be easily used and provides good performance from the designing properties and the robustization possibilities points of view, respectively. As our results showed, the framework provides appropriate results for tumor control.

The application of the Soft Computing based methods, especially, the Tensor Product (TP) transformation has several beneficial properties from the biological modeling and control point of view, because complex, nonlinear processes can be handled by them effectively. Another advantage of these tools consist on the Linear Parameter Varying (LPV) and Linear Matrix Inequality (LMI) based techniques can be easily connected to them. The aim of this study is to develop TP models, which can describe the tumor growth beside anti-angiogenic treatment. The role of the anti-angiogenic therapies is to decrease the size of the tumor to operable or maintainable level. From control engineering point of view, the treatment process can be formulated as a control task. In this work, we realized two TP models, which approximates the initial transformed model with high accuracy, regardless the kind of input load and without stability problems. The TP models will be used for TP-based controller design on LMI basis.

In case of physiological related researches the appropriate adjustment of the parameters of the mathematical models describing biological phenomenons is a crucial issue. These models are essential in many research field such as the personalized health care or the control of physiological processes. Despite the available identification techniques there is no general solution in those cases where the mathematical model is given, but highly nonlinear in order to capture the main dynamical attitude of the physiological processes to be described. One of our aims was to develop such a general nonlinear identification framework which is flexible, can be easily used and supports the identification of these kind of models. We defined different metrics to measure the performance of the developed system. From the other hand, our goal was to successfully realize the identification framework in case of tumor growth beside anti-angiogenic treatment which is essential in our future work in order to validate the performance of advanced control algorithms. The results show that the nonlinear identification framework performed well in this case, since the predefined requirements from the applied metrics points of view were satisfied in all cases.

Nowadays, several databases store information about patients and diseases, but only a few exists that focus directly on myocardial events and treatments. This paper is divided into two parts. In the first part, we list and summarize the ongoing European myocardial projects (Myocardial Ischaemia National Audit Project (MINAP) in England, Swedish Web-system for Enhancement and Development of Evidence-based care in Heart disease Evaluated According to Recommended Therapies (SWEDEHEART) in Sweden, National Registry of Acute Myocardial Infarction in Switzerland (AMIS Plus) in Switzerland). Where possible, we discuss the validity and accuracy of the stored data. In the second part, we introduce the history and legal environment of the Hungarian Myocardial Infarction Registry (HUMIR), and some research results that were achieved with the help of the information in the Hungarian registry.

Due to increasing prevalence of diabetes as well as increasing management costs, the artificial control of diabetes is a highly important task. Model-based design allows finding more effective solutions for the individual treatment of diabetic patients, but robustness is an important property that can be hardly guaranteed by the already developed individualized control algorithms. Modern robust control (known as H ∞ ) theory represents an efficient possibility to solve robustness requirements in a general way based on exact mathematical formulation (Linear Matrix Inequalities) combined with knowledge-based expertise (through real patient data, uncertainty weighting functions can be formulated). When the difference between the nominal model and real patient dynamics is bounded and known, this approach becomes highly reliable. However, this requirement poses the greatest limitation since a model always represents an approximation of the complex physiological process. Consequently, the uncertainty formulation of the neglected dynamics becomes crucial as robust methods are very sensitive to them. In order to formulate them, large amount of real patient data and medical expertise is needed to cover the different life-style scenarios (especially the worst-case ones) that define the control space by the accumulated knowledge. On the other hand, H ∞ –based methods represent linear control techniques; hence their direct nonlinear application is important for a physiological process. The paper presents a roadmap of using modern robust control in diabetes focusing on nonlinear model-based interpretation: how the weighting functions should be selected based on (knowledge-based) medical expertise, the direct nonlinear applicability of the method taking additional advantage of the recently emerged Linear Parameter Varying (LPV) methodology, robust performance investigation and switching control possibilities. During the control characteristics discussion, the trade-off between the medical knowledge-based empiricism and exact control engineering formulation is introduced through different examples computed under MATLAB on real diabetic patient data.

A general approach to solve the inverse kinematics problem of series manipulators, i.e. finding the required joint motions for the desired end effector motions, is based on the linear approximation of the forward kinematics map and discretization of the continuous problem. Due to the linearization, first velocities are calculated, so numerical integration needs to be done to get the joint variables. This general solution is just a numerical approximation, thus improving the tracking performance of the inverse kinematics algorithm is of great importance. The application of several numerical integration techniques (implicit Euler, explicit trapezoid, implicit trapezoid) is analyzed, and a fix point iteration is given that can be used to calculate implicit solutions. The tracking performance of the spatial inverse positioning problem of a spatial manipulator is analyzed by checking the tracking error in the desired direction (i.e. along the derivative of the desired end effector path) and in the plane perpendicular to the desired direction. The application of the explicit and implicit trapezoid methods yielded much better tracking performance in the directions orthogonal to the desired direction when the end effector had to track a linear path, while the tracking performance in the desired direction was similar for all the methods. Simulations showed that the application of implicit and second-order methods in the numerical integration may greatly improve the tracking performance of the closed-loop inverse kinematics algorithm.

In-silico modeling is an important part of biomedical engineering. Advanced controllers providing high quality control can be validated through it checking if the available mathematical model of the given biomedical process produces the desired output. However, due to high patient variability the advanced linear control methods applied on linearized models could produce several distortions compared to the original nonlinear models; hence, these errors should be reduced. Hierarchical control strategies could be a possibility or from modeling point of view using different control-oriented modeling methodologies. Linear Parameter Varying (LPV) approaches with Linear Matrix Inequality (LMI) based modeling and controller design represent one choice. In this paper, we investigate their generalized extension, the Tensor Product (TP) model transformation demonstrated on diabetes modeling. In concrete, the type 1 diabetes modeling on Intensive Care Units (ICU) is envisaged. The achieved results will be used for TP transformation based controller design in our later work.

Antiangiogenic inhibitors offer a promising new treatment modality in oncology. However, the optimal administration regime is often not well-established, despite the fact that it might have substantial impact on the outcome. The aim of the present study was to investigate this issue. Eight weeks old male C57Bl/6 mice were implanted with C38 colon adenocarcinoma, and were given either daily (n = 9) or single (n = 5) dose of bevacizumab; both receiving the same dose the only difference being the administration pattern. Outcome was measured by tracking tumor volume; both caliper and magnetic resonance imaging was employed. Longitudinal growth curves were modelled with mixed-effects models (with correction for autocorrelation and heteroscedasticity, where necessary) to infer on population-level. Several different growth models (exponential, logistic, Gompertz) were applied and compared. Results show that the estimation of the exponential model is very reliable, but it prevents extrapolation in time. Nevertheless, it clearly established the advantage of the continuous regime.

In software development application lifecycle management (ALM) systems are used to support the development process. As these products are tailored for best fitting the applied programs and their actual usage is diverse. Often, this means that products of different vendors are applied, which reduce the reachability between different artefacts and the overall consistency of the system. In this paper we are analyzing the applicability of Augmented Lifecycle Space as a new approach. This approach provides the capability to enhance the traceability and consistency while reducing human effort through automation both in homogeneous and heterogeneous tool environments.

Tensor Product (TP) transformation based modeling and control can be useful in biomedical engineering, since complex nonlinear control tasks can be handled easier with it. Moreover, the modeling approach can handle the Linear Parameter Varying (LPV) models and produces a tensor based system description, which can be used during Linear Matrix Inequality (LMI) based controller design. The TP property makes the usability of the method beneficial as LMI connected techniques allows using the Lyapunov theorems. The aim of the current work is to demonstrate the usability of TP models in biomedical applications, i.e. diabetes modeling. The core model, the minimal model is investigated and simulation results are presented under Matlab.

In this paper we present the plan, motivation, background, and the design of an agent-based simulation framework describing the spread of Hospital-Associated Infections (HAIs). We are developing a general simulation environment that is able to model wide range of pathogen transmission scenarios in hospital environment. The elements of the simulation include among others: admission and discharge patients, pathogen transmission via healthcare workers, colonization and infection, modelling hospital events, scheduling treatments, the interventions against HAI spreading. The evolution of the model is tracked in discrete time, and the simulation is driven by stochastic events sampled from predefined distributions. Our aim is to build a general, customisable and extensible simulation environment for the domain of HAIs, therefore the presented design is in Object-Oriented fashion. We implement the system in R using S4 classes, although the design is general. The results of the simulations are time series and transmission networks.

In the medical practice, there are several methods to administer anti-cancer drugs. A commonly used method is the intermittent bolus doses (BD) administration when the patient receives drug on given days and the therapy has rest periods between the injections. The amount of bolus doses can be the maximum tolerated dose (MTD) or less. Anti-cancer drug can be administered in low doses over prolonged periods without extended rest periods which is called as low-dose metronomic therapy (LDM). In addition, continuous infusion therapy is applicable within clinical environment, not yet as a portable device. The major disadvantage of these methods is the empiricism associated with determining the optimal biologic dose (OBD). In order to solve the problem, we have designed discrete-time controllers which realize automated optimal treatments.

Application lifecycle management (ALM) systems, used to support software development processes, can be composed from various components depending on the need of company. In this paper we focus on benefits of establishment of cross-tool interoperability with specific regard to heterogeneous platforms. We discuss how the different components can be connected for systemic tool collaboration with the help of Open Services for Lifecycle Collaboration (OSLC). This helps to improve traceability and consistency. Furthermore, workflow can be generated to correct the traceability gaps, consistency problems and even to implement modifications. Moreover, we present how the workload of developers can be decreased by integrating revision control system and development environment with the existing ALM system.

Adaptive, model-free control of Type 1 Diabetes Mellitus (T1DM) is a lack in the field of diabetes control, since, most of the applied control strategies are model-based ones. The main problem is that difficult to formulate exact mathematical models to replicate the physiological processes, not just because of their behavior, rather then these processes are changing patient-by-patient. Furthermore, the developed models so far, are highly non-linear and difficult to manage. A possible adaptive control solution can be the recently developed Robust Fixed Point Transformation (RFPT)-based control design method, which can provide control action, based on the observations about the actual output of a controlled system. In this paper we show a survey, how can be used this novel technique related with a known, highorder glucose-insulin model, to investigate the usability according to diabetes control.

With blood purification the life of a person suffering by kidney malfunction can be saved or the quality of her/his life can be increased. This is the purpose of hemodialysis machines, where the blood of the patient is filtered (cleared) in an extracorporeal tube system. Here peristaltic pumps are responsible for the fluid transport, where the strict control is a serious need, in order to maintain patient fluid balance and meet exact dosage. The current paper focuses on designing a robust controller with TP transformation. This controller is presented from the application point of view. The experiences with the designed controller are tested on a real system discussing their applicability.

Tumor growth model identification under antiangiogenic therapy is a very current issue since the existing models in the literature have some limitations and usually they are not clinically validated. We have carried out animal experiments to observe valid data, mice were transplanted with C38 colon Aden carcinoma and they were treated with bevacizumab. Two groups were created, control group was treated according to the protocol, while case group members receive much lower doses daily. We created fixed and mixed models for the groups. Mixed models differs from fixed ones in random effects -- in the case of mixed models both the intercept and the slope are random variables. These models are appropriate when the aim is to model not the concrete subjects in the sample, but rather, to describe the imagined population from which the samples were coming.

Benchmark problems continue to represent an actively studied domain, focusing on application-based situations, where controllers have to deal with typical real environments. In this paper, a Robust Fixed Point Transformations (RFPT)-based Model Reference Adaptive Controller (MRAC) is designed for a modified Translational Oscillations by a Rotational Actuator (TORA) system, which is an indirectly driven, underactuated classical mechanical system with peculiar properties. The RFPT-based design has the advantage of working only with three free parameters, and does not need complex a priori calculations. It is founded on the idea that at the cost of replacing the requirement for global stability with local stability, a mathematically very simple and geometrically lucid, well interpreted methodology can be developed. The resulting structure directly concentrates on the primary design intent, i.e., on the realization of a purely kinematically prescribed trajectory tracking. Examples and simulation results are presented in this paper, demonstrating that the RFPT-based design can provide an efficient MRAC controller for a very special physical system.

A composite model, describing the glucose/insulin dynamics following daily food administration and insulin injections in Type 1 Diabetes Mellitus patients is presented. Three daily meals have been simulated, food intake representing four different types of foodstuffs, along with three rapid-acting insulin injections and one long-acting insulin injection. Three different scenarios (depending on whether food intake and/or administration times were fixed or random) were hypothesized: simulations show a very realistic time-course for both glucose and insulin dynamics over long (20 days) and short (one day) time periods.

In physiological control there is a need to estimate signals that cannot be measured directly. Burdened by measurement noise and unknown disturbances this proves to be challenging, since the models are usually highly nonlinear. Sigma-point filters could represent an adequate choice to overcome this problem. The paper investigates the applicability of several different versions of sigma-point filters for the Artificial Pancreas problem on the widely used Cambridge (Hovorka)-model.

Model Reference Adaptive Controllers (MRAC) have dual functionality: besides guaranteeing precise trajectory tracking of the controlled system, they have to provide an “external control loop” with the illusion that it controls a physical system of prescribed dynamic properties, i.e., the “reference system”. The MRACs are designed traditionally by Lyapunov's 2ndmethod that is mathematically complicated, requiring strong skills from the designer. Adaptive controllers alternatively designed by the use of Robust Fixed Point Transformations (RFPT) operate according to Banach's Fixed Point Theorem, and are normally simple iterative constructions that also have a standard variant for MRAC design. This controller assumes a single actuator that is driven adaptively. Master-Slave Systems form a distinct class of practical applications, in which two arms-the master and the slave-operate simultaneously. The movement of the master must be tracked precisely by the slave in spite of the quite different forces exerted by them. In the present paper, a soft tissue-cutting operation by a master-slave structure is simulated. The master arm has a simple torque-reference friction model, and is driven by the surgeon. The obtained master arm trajectory has to be precisely tracked by the electric DC motor driven slave system, which is in dynamic interaction with the actual tissue under operation. It is shown via simulations that the RFPT-based design can efficiently solve such tasks without considerable mathematical complexity.

The paper presents a proof-of-concept shape-based visual recognition method with efficient local implementation for mobile devices, not relying on network or cloud processing. In such situations the focus is on effectiveness and simplicity, preserving a high level of functionality. Applications include offline object recognition and template matching (e.g. authorization, blind aid, product recognition).

Image-guided surgery offers great advantages to surgeons through the possibility to track tools in 3D space and to navigate based on the virtual model of the patient. In the case of robot-assisted procedures, both the inherent accuracy of the system components and the quality of the registration procedures are critical to provide high precision treatment delivery. One of the major barriers towards more technology-integrated procedures is the fact that alterations in the operating room environment can fundamentally change the performance of the system, decrease the accuracy, and therefore pose significant danger to the patient. Surgical events from the control point of view may include motion of the robot, motion of the camera, or motion of the patient. The paper describes a new concept to treat these events, to track and automatically compensate for abrupt changes that may affect the accuracy of a robot-integrated interventional system. Our solution is to use all available information at a given time, including the intra-operative tracker's internal base frame, to distinguish between different surgical events. The concept has been developed and tested on the neurosurgical robot system at the Johns Hopkins University. Initial experiments performed on data recordings from simulated scenarios showed that the algorithm was able to correctly identify the cases.

The paper introduces a video surveillance and event detection framework and application for semi-supervised surveillance use. The systempsilas intended use is in automatic mode on camera feeds that are not actively watched by surveillance personnel, and should raise alarms when unusual events occur. We present the current detector filters, and the extendable modular interface. Filters include local and global unusual motion detectors, left/stolen object detector, motion detector, tampering/failure detector, etc. The system stores the events and associated data, which can be organized, searched, annotated and (re)viewed. It has been tested in real life situation for police street surveillance.

We present an automatic focus area estimation method, working with a single image without a priori information about the image, the camera, or the scene. It produces relative focus maps by localized blind deconvolution and a new residual error-based classification. Evaluation and comparison is performed and applicability is shown through image indexing.