Search

Background: Plasma concentrations of gut microbial metabolites are associated with cardiomyocytes viability and platelet reactivity. We hypothesised that increased concentrations of gut metabolites may predict major adverse cardiovascular events (MACE) after acute myocardial infarction (AMI). Methods: We compared plasma concentrations of gut metabolites (trimethylamine-N-oxide, TMAO and indoxyl sulphate, IS) and platelet reactivity in 60 patients with AMI and 27 healthy controls. We assessed the predictive value of gut metabolites for MACE (stroke, recurrent AMI, death) over a median of 3.5-years. Results: The concentrations of TMAO and IS did not differ between AMI patients and controls. The concentrations of TMAO and IS were higher in patients who developed MACE, compared to those who did not (p≤0.015 for all). The concentration of TMAO was the only independent predictor of MACE in a multivariate analysis (OR 35.041, CI 1.269-967.307, p=0.036). Patients with the concentration of TMAO and indoxyl sulphate above the cut-off value predictive of MACE had higher platelet activity (p≤0.149 for all). Conclusions: Increased plasma concentration of TMAO is the independent predictor of MACE and may contribute to post-AMI cardiac dysfunction.

Stroke is the second main cause of mortality and the third leading cause of mortality and permanent disability combined. Many potential biomarkers have been described to contribute to the diagnosis, prognosis of outcomes, and risk stratification after stroke. Copeptin is an inactive peptide that is produced in an equimolar ratio to arginine vasopressin (AVP) in response to the activation of the endogenous stress system.The present study isa systematic review and meta-analysis to assess plasma copeptin concentrations, diagnostic and prognostic values for risk stratification after acute ischemic stroke and transient ischemic attack.Mean copeptin level in stroke vs. non-stroke groups varied and amounted to 19.8 ± 17.4 vs. 9.7 ± 6.6 pmol/L, respectively (mean differences [MD]: 12.75; 95% confidence interval [CI]: 5.00 to 20.49; p0.001), in good vs. poor outcome 12.0 ± 3.6 vs. 29.4 ± 14.5 (MD: -8.13; 95% CI: -8.37 to -7.88; p0.001) and in survive vs. non-survive stroke patients: 13.4 ± 3.2 vs. 33.0 ± 12.3, respectively (MD: -13.43; 95% CI: -17.82 to -9.05; p0.001).The above systematic review and meta-analysis suggests that monitoring the copeptin levels may help predict the long-term prognosis of ischemic stroke efficiently. Determining the copeptin level may help individualize the management of ischemic stroke patients, keep stroke risk lower, reduce post-stroke complications, including patient death, and minimize healthcare costs.

Background:Oxygen uptake (VO2) is one of the most important measures of fitness and critical vital sign. Cardiopulmonary exercise testing (CPET) is a valuable method of assessing fitness in sport and clinical settings. There is a lack of large studies on athletic populations to predict VO2max using somatic or submaximal CPET variables. Thus, this study aimed to: (1) derive prediction models for maximal VO2 (VO2max) based on submaximal exercise variables at anaerobic threshold (AT) or respiratory compensation point (RCP) or only somatic and (2) internally validate provided equations.Methods:Four thousand four hundred twenty-four male endurance athletes (EA) underwent maximal symptom-limited CPET on a treadmill (n=3330) or cycle ergometer (n=1094). The cohort was randomly divided between: variables selection (nrunners = 1998; ncyclist = 656), model building (nrunners = 666; ncyclist = 219), and validation (nrunners = 666; ncyclist = 219). Random forest was used to select the most significant variables. Models were derived and internally validated with multiple linear regression.Results:Runners were 36.24±8.45 years; BMI = 23.94 ± 2.43 kg·m−2; VO2max=53.81±6.67 mL·min−1·kg−1. Cyclists were 37.33±9.13 years; BMI = 24.34 ± 2.63 kg·m−2; VO2max=51.74±7.99 mL·min−1·kg−1. VO2 at AT and RCP were the most contributing variables to exercise equations. Body mass and body fat had the highest impact on the somatic equation. Model performance for VO2max based on variables at AT was R2=0.81, at RCP was R2=0.91, at AT and RCP was R2=0.91 and for somatic-only was R2=0.43.Conclusions:Derived prediction models were highly accurate and fairly replicable. Formulae allow for precise estimation of VO2max based on submaximal exercise performance or somatic variables. Presented models are applicable for sport and clinical settling. They are a valuable supplementary method for fitness practitioners to adjust individualised training recommendations.Funding:No external funding was received for this work.

ObjectivesOxygen uptake (VO2) is one of the most important measures of fitness and critical vital sign. CPET is a valuable method of assessing fitness in sport and clinical settings. This study aimed to: (1) derive prediction models for maximal VO2(VO2max) based on exercise variables at anaerobic threshold (AT) or respiratory compensation point (RCP) or only somatic and (2) internally validate provided equations.Methods4424 male endurance athletes (EA) underwent maximal symptom-limited CPET on a treadmill (n=3330) or cycle ergometer (n=1094). The cohort was randomly divided between: variables selection (nrunners=1998; ncyclist=656), model building (nrunners=666; ncyclist=219) and validation (nrunners=666; ncyclist=219). Random Forest was used to select the most significant variables. Models were derived and internally validated with Multiple Linear Regression.ResultsRunners were 36.24±8.45 yrs.; BMI=23.94±2.43 kg·m−2; VO2max=53.81±6.67 mL·min−1·kg−1. Cyclists were 37.33±9.13 yr.; BMI=24.34±2.63 kg·m−2; VO2max=51.74±7.99 mL·min−1·kg−1. VO2at AT and RCP were the most contributing variables to exercise equations. Body mass and body fat had the highest impact on the somatic equation. Model performance for VO2maxbased on variables at AT was R2=0.81, at RCP was R2=0.91, at AT&RCP was R2=0.91 and for somatic-only was R2=0.43.ConclusionsDerived prediction models were highly accurate and fairly replicable. Formulae allow for precise estimation of VO2maxbased on submaximal exercise performance or somatic variables. Presented models are applicable for sport and clinical settling. They are a valuable supplementary method for fitness practitioners to adjust individualised training recommendations.FundingThe author(s) received no specific funding for this work.

(1) Background: Pulmonary embolism (PE) is the third most frequent acute cardiovascular condition worldwide. PE response teams (PERTs) have been created to facilitate treatment implementation in PE patients. Here, we report on the 5-year experience of PERT operating in Warsaw, Poland, with regard to the characteristics and outcomes of the consulted patients. (2) Methods: Patients diagnosed with PE between September 2017 and December 2021 were included in the study. Clinical and treatment data were obtained from medical records. Patient outcomes were assessed in-hospital, at a 1- and 12-month follow-up. (3) Results: There were 235 PERT activations. The risk of early mortality was low in 51 patients (21.8%), intermediate–low in 83 (35.3%), intermediate–high in 80 (34.0%) and high in 21 (8.9%) patients. Anticoagulation alone was the most frequently administered treatment in all patient subgroups (altogether 84.7%). Systemic thrombolysis (47.6%) and interventional therapy (52%) were the prevailing treatment options in high-risk patients. The in-hospital mortality was 6.4%. The adverse events during 1-year follow-up included five deaths, two recurrent VTE and two minor bleeding events. (4) Conclusions: Our initial 5-year experience showed that the activity of the local PERT facilitated patient-tailored decision making and the access to advanced therapies, with subsequent low overall mortality and treatment complication rates, confirming the benefits of PERT implementation.

Currently, quite accurate measurements of atmospheric gusts are carried out by airport systems only in the vicinity of the runways. There is a still open issue of availability of information about real wind gusts at cruising altitudes and during approach at a considerable distance from the airfield. Standard on-board systems of a jet transport airplane provide some information which is desirable to have knowledge of how flight parameters reflect real gust parameters and their impact on the aircraft dynamics. The paper proposes an algorithm for headwind gust magnitude estimation in relation to aircraft response. The analysed estimation algorithms assume the use of data available from the existing on-board systems only without the employment of any extra sensors or ground and satellite systems. In this way, many problems caused by different structures, configurations, and ways of installation of additional sensors and structural changes are rejected. The algorithms use the classical method for estimation of wind parameters as well as a linear longitudinal model of aircraft dynamics, taking into account the influence of wind gusts. Data fusion was realised with the use of three filtration methods. Results were evaluated to select the most accurate method of the estimation. Test data were obtained from advanced flight simulation. The experimental scenario considered a flight of a passenger twin-engine jet airplane through a layer of programmed gusts. The results of the flight simulations allowed us to determine the accuracy of the proposed gust estimation algorithms in reference to the ideal wind-speed data analysis obtained directly from the simulation environment (with the accuracy of the simulation process). The use of the proposed gust estimation algorithms may provide more accurate signal for integrated on-board systems, especially for wind shear detection and sped-up response time of flight control systems, protecting aircrafts against the adverse impact of encountered wind shear or gusts, e.g., auto-thrust or auto-throttle systems. The dedicated algorithm presented in the paper may increase the safety level of take-off and approach phases in gusty conditions and also during significant changes in wind speed at cruising altitudes in the case of crossing the area of jet stream occurrence.

Acute heart failure (AHF) is a sudden, life-threatening condition, defined as a gradual or rapid onset of symptoms and/or signs of HF. AHF requires urgent medical attention, being the most frequent cause of unplanned hospital admission in patients above 65 years of age. AHF is associated with a 4-12% in-hospital mortality rate and a 21-35% 1-year mortality rate post-discharge. Considering the serious prognosis in AHF patients, it is very important to understand the mechanisms and haemodynamic status in an individual AHF patient, thus preventing end-organ failure and death. Haemodynamic monitoring is a serial assessment of cardiovascular function, intended to detect physiologic abnormalities at the earliest stages, determine which interventions could be most effective, and provide the basis for initiating the most appropriate therapy and evaluate its effects. Over the past decades, haemodynamic monitoring techniques have evolved greatly. Nowadays, they range from very invasive to non-invasive, from intermittent to continuous, and in terms of the provided parameters. Invasive techniques contain pulmonary artery catheterization and transpulmonary thermodilution. Minimally invasive techniques include oesophageal Doppler and noncalibrated pulse wave analysis. Non-invasive techniques contain echocardiography, bioimpedance, and bioreactance techniques as well as non-invasive pulse contour methods. Each of these techniques has specific indications and limitations. In this article, we aimed to provide a pathophysiological explanation of the physical terms and parameters used for haemodynamic monitoring in AHF and to summarize the working principles, advantages, and disadvantages of the currently used methods of haemodynamic monitoring.

Multiple sclerosis (MS) is a central nervous system chronic neuroinflammatory disease followed by neurodegeneration. The diagnosis is based on clinical presentation, cerebrospinal fluid testing and magnetic resonance imagining. There is still a lack of a diagnostic blood-based biomarker for MS. Due to the cost and difficulty of diagnosis, new and more easily accessible methods are being sought. New biomarkers should also allow for early diagnosis. Additionally, the treatment of MS should lead to the personalization of the therapy. MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) as well as their target genes participate in pathophysiology processes in MS. Although the detailed mechanism of action of non-coding RNAs (ncRNAs, including miRNAs and lncRNAs) on neuroinflammation in MS has not been fully explained, several studies were conducted aiming to analyse their impact in MS. In this article, we review up-to-date knowledge on the latest research concerning the ncRNAs in MS and evaluate their role in neuroinflammation. We also point out the most promising ncRNAs which may be promising in MS as diagnostic and prognostic biomarkers.

The SARS-CoV-2 (COVID-19) pandemic is a major issue that necessitates the use of cutting-edge disease prediction models. The aim of the study was to assess the existing evidence regarding association between Krebs von den Lungen-6 levels and COVID-19 severity. A literature search was performed on Web of Science, PubMed, Scopus and Cochrane Central Register of Controlled Trials databases from 1 January 2020 up to 2 August 2022. The electronic database search was supplemented by searching Google Scholar. In addition, reference lists of relative articles were also reviewed. KL-6 levels among COVID-19 positive vs. negative patients varied and amounted to 443.37 ± 249.33 vs. 205.73 ± 86.8 U/mL (MD = 275.33; 95%CI: 144.57 to 406.09; p < 0.001). The KL-6 level was 402.82 ± 261.16 U/mL in the severe group and was statistically significantly higher than in the non-severe group (297.38 ± 90.46 U/mL; MD = 192.45; 95%CI: 118.19 to 266.72; p < 0.001). The KL-6 level in the mild group was 272.28 ± 95.42 U/mL, compared to 268.04 ± 55.04 U/mL in the moderate COVID-19 group (MD = −12.58; 95%CI: −21.59 to −3.57; p = 0.006). Our meta-analysis indicates a significant association between increased KL-6 levels and SARS-CoV-2 infection. Moreover, KL-6 levels are significantly higher in patients with a more severe course of COVID-19, indicating that KL-6 may be a useful predictor to identify patients at risk for severe COVID-19.

Aleksandra G&aogon;secka,1 Piotr Szwed,1 Karolina Jasi&nacute;ska,1 Oliwia Fidali,1 Aleksandra K&lstrok;&eogon;bukowska,1 Ceren Eyileten,2 Marek Postula,2 &Lstrok;ukasz Szarpak,3,4 Tomasz Mazurek,1 Grzegorz Opolski,1 Krzysztof J Filipiak,1 Marcin Ufnal5 1 1st Chair and Department of Cardiology, Medical University of Warsaw, Warsaw, 02-097, Poland; 2Department of Experimental and Clinical Pharmacology, Centre for Preclinical Research and Technology, Medical University of Warsaw, Warsaw, 02-097, Poland; 3Bialystok Oncology Center, Bialystok, 15-027, Poland; 4Maria Sklodowska-Curie Medical Academy in Warsaw, Warsaw, 03-411, Poland; 5Department of Experimental Physiology and Pathophysiology, Centre for Preclinical Research and Technology, Medical University of Warsaw, Warsaw, 02-106, PolandCorrespondence: Aleksandra G&aogon;secka 1st Chair and Department of Cardiology, Medical University of Warsaw, Banacha 1a, Warsaw, 02-097, PolandTel +48 22 599 19 51Email aleksandra.gasecka@wum.edu.plPurpose: Acute myocardial infarction (AMI) is the leading cause of morbidity and mortality worldwide. Damage to the endothelium is the earliest event in atherothrombosis, including AMI. Nitric oxide (NO), an endothelium-derived compound, protects the vasculature from damage. This study evaluated whether an association exists between plasma concentration of endogenous NO-related pathway metabolites linked to AMI and major adverse cardiovascular events (MACE) after AMI.Methods: We compared plasma concentrations of NO-related pathway metabolites in patients after AMI (n=60) and healthy controls (n=27) and investigated the prognostic value of these metabolites for post-AMI MACE development over a median of 3.5-years. In search of biomarkers, we compared plasma concentrations of dimethylarginines (ADMA, SDMA), citrulline, arginine and ornithine using ultra performance liquid chromatograph coupled with a mass spectrometer.Results: Patients after AMI had higher concentrations of dimethylarginines, compared to controls (p=0.0068, p< 0.0001, respectively). Conversely, the concentration of citrulline was lower in the AMI group (p=0.0006). The concentration of SDMA was higher in patients who developed MACE than in those who did not (p=0.015). SDMA was the only independent predictor of MACE in multivariate analysis (p=0.023). There was an intermediate, negative correlation between plasma SDMA level and platelet reactivity (r=− 0.33, p=0.02).Conclusion: Plasma concentration of dimethylarginines differs between patients with AMI and healthy volunteers. The study’s novel finding is that SDMA is an independent predictor of MACE during a 3.5 year follow-up period after AMI.Keywords: nitric oxide, L-arginine, acute myocardial infarction, major adverse cardiovascular events, prognosis

Atherosclerotic cardiovascular diseases (ASCVD) are the major cause of mortality worldwide. Despite the continuous progress in ASCVD therapy, the residual risk persists beyond the management of traditional risk factors. Several infections including Helicobacter pylori infection, periodontal disease, and viral infections are associated with the increased risk of ASCVD, both directly by damage to the heart muscle and vasculature, and indirectly by triggering a systemic proinflammatory state. Hence, beyond the optimal management of the traditional ASCVD risk factors, infections should be considered as an important non-classical risk factor to enable early diagnosis and appropriate treatment. Here, we summarized the currently available evidence regarding the role of inflammation in ASCVD and the association between the particular infections and pathogens (Helicobacter pylori, periodontal disease, pneumonia, Cytomegalovirus, Human immunodeficiency virus, Herpes simplex virus, and severe acute respiratory syndrome coronavirus 2) on the development and progression of ASCVD. We also speculated about the potential therapeutic implications of the anti-inflammatory and anti-infective drugs on ASCVD outcomes, including drugs routinely administered in patients with ASCVD (statins, P2Y12 receptor inhibitors, and angiotensin-converting enzyme inhibitors/angiotensin receptor blockers) and novel strategies aiming at residual risk reduction (colchicine, anti-cytokine drugs, and methotrexate). Considering the emerging association between infections and ASCVD, it is crucial to determine the possible advantages of infection prevention and treatment in patients with ASCVD.

Fuzzy Cognitive Maps (FCMs) are considered a soft computing technique combining elements of fuzzy logic and recurrent neural networks. They found multiple application in such domains as modeling of system behavior, prediction of time series, decision making and process control. Less attention, however, has been turned towards using them in pattern classification. In this work we propose an FCM based classifier with a fully connected map structure. In contrast to methods that expect reaching a steady system state during reasoning, we chose to execute a few FCM iterations (steps) before collecting output labels. Weights were learned with a gradient algorithm and logloss or cross-entropy were used as the cost function. Our primary goal was to verify, whether such design would result in a descent general purpose classifier, with performance comparable to off the shelf classical methods. As the preliminary results were promising, we investigated the hypothesis that the performance of d -step classifier can be attributed to a fact that in previous d − 1 steps it transforms the feature space by grouping observations belonging to a given class, so that they became more compact and separable. To verify this hypothesis we calculated three clustering scores for the transformed feature space. We also evaluated performance of pipelines built from FCM-based data transformer followed by a classification algorithm. The standard statistical analyzes confirmed both the performance of FCM based classifier and its capability to improve data. The supporting prototype software was implemented in Python using TensorFlow library.

Various country-wide guidelines for speed limits setting make use of diverse parameters specifying properties of roads, their environment, as well as traffic intensity, statistical distribution of measured vehicle speeds and history of crash accidents. We argue, that although such extensive data are not present in public geospatial datasets, they can be inferred as latent features in machine learning models trained to predict speed limits. To verify this hypothesis we performed an experiment, in which we extracted tag-based and geometrical features from Open Street Map for Poland and applied various classification methods to predict class labels corresponding to speed limits. In spite of the fact that the datasets were imbalanced (with majority classes corresponding to default speed limits) we obtained F1 scores ranging from 0.72 to 0.91 for lower speed roads. The neural network classifier implemented with Tensorflow framework turned out to be the most efficient.

This paper presents a multi-swarm PSO algorithm for the Quadratic Assignment Problem (QAP) implemented on the OpenCL platform. Our work was motivated by results of time efficiency tests performed for single-swarm algorithm implementation that showed clearly that the benefits of a parallel execution platform can be fully exploited provided the processed population is large. The described algorithm can be executed in two modes: with independent swarms or with migration. We discuss the algorithm construction as well as we report results of tests performed on several problem instances from the QAPLIB library. During the experiments the algorithm was configured to process large populations. This allowed us to collect statistical data related to values of goal function reached by individual particles. We use them to demonstrate on two test cases that although single particles seem to behave chaotically during the optimization process, when the whole population is analyzed, the probability that a particle will select a near-optimal solution grows.

Nowadays, in the majority of cases, speed limits are defined by experts, who take into account the qualitative and quantitative parameters of the examined roads. The process of setting speed limits is often complex, because technical, social, and legal aspects should be taken into account. The paper presents a developed rule-based system for setting maximum speed limits, and results of the research with an unified data model describing roads and their surroundings. Analysis of the possibility of obtaining relevant data and automation of the process of their collection are also described. The set of the rules was developed on the basis of the methodologies used in Poland, United States, Germany, and New Zealand.

Authorship attribution aims at identifying the author of an unseen text document based on text samples originating from different authors. In this paper we focus on authorship attribution of Polish texts using stylometric features based on part of speech (POS) tags. Polish language is characterized by high inflection level and in consequence over 1000 POS tags can be distinguished. This allows building a sufficiently large feature space by extracting POS information from documents and performing their classification with use of machine learning methods. We report results of experiments conducted with Weka workbench using combinations of the following features: POS tags, an approximation of their bigrams and simple document statistics.

Authorship attribution aims at distinguishing texts written by different authors using text features representing their styles. In this paper we investigate stylometric features for the Polish language based on Part of Speech (POS) tagging (including POS bigrams) and function words. Due to high inflection level of Polish language the feature space tends to be very large. This in particular concerns POS n-grams. Focusing on POS bigrams, we propose their simplified representation allowing to keep the feature space compact. We report experiments, in which authorship attribution was conducted for varying in lengths documents, with use of classifiers from the Weka library. We evaluate classification results for combinations of the following features: POS tags, POS bigrams, function words and simple document statistics. Experiments indicate that the developed features provide good classification performance.

For various IT systems security is considered a key quality factor. In particular, it might be crucial for video surveillance systems, as their goal is to provide continuous protection of critical infrastructure and other facilities. Risk assessment is an important activity in security management; it aims at identifying assets, threats and vulnerabilities, analysis of implemented countermeasures and their effectiveness in mitigating risks. This paper discusses an application of a new risk assessment method, in which risk calculation is based on Fuzzy Cognitive Maps (FCMs) to a complex automated video surveillance system. FCMs are used to capture dependencies between assets and FCM based reasoning is applied to aggregate risks assigned to lower-level assets (e.g. cameras, hardware, software modules, communications, people) to such high level assets as services, maintained data and processes. Lessons learned indicate, that the proposed method is an efficient and low-cost approach, giving instantaneous feedback and enabling reasoning on effectiveness of security system.

For contemporary software systems, security is considered to be a key quality factor and the analysis of IT security risk becomes an indispensable stage during software deployment. However, performing risk assessment according to methodologies and standards issued for the public sector or large institutions can be too costly and time consuming. Current business practice tends to circumvent risk assessment by defining sets of standard safeguards and applying them to all developed systems. This leads to a substantial gap: threats are not re-evaluated for particular systems and the selection of security functions is not based on risk models. This paper discusses a new lightweight risk assessment method aimed at filling this gap. In this proposal, Fuzzy Cognitive Maps (FCMs) are used to capture dependencies between assets, and FCM-based reasoning is performed to calculate risks. An application of the method is studied using an example of an e-health system providing remote telemonitoring, data storage and teleconsultation services. Lessons learned indicate that the proposed method is an efficient and low-cost approach, giving instantaneous feedback and enabling reasoning on the effectiveness of the security system.

This paper presents a system for extraction of concepts from unstructured Polish texts. Here concepts are understood as n-grams, whose words satisfy specific grammatical constraints. Detection and transformation of concepts to their normalized form are performed with rules defined in a language, which combines elements of colored and fuzzy Petri nets. We apply a user friendly method for specification of samples of transformation patterns that are further compiled to rules. To improve accuracy and performance, we recently introduced rule management mechanisms, which are based on two relations between rules: partial refinement and covering. The implemented methods include filtering with metarules and removal of redundant rules (i.e. these covered by other rules). We report results of experiments, which aimed at extracting specific concepts (actions) using a ruleset refactored with the developed rule management techniques.

This paper addresses the problem of modeling and automated recognition of video events. We propose to use Linear Temporal Logic as a language for events specification and Fuzzy Semantic Petri Nets (FSPN) as a tool for their recognition. FSPN are Petri nets coupled with an underlying fuzzy ontology. The ontology stores assertions (facts) concerning classification of objects and detected relations. Fuzzy predicates querying the ontology content are used as guards of transitions in FSPN. Tokens carry information on objects participating in a scenario and are equipped with weights indicating likelihood of their assignment to places. In turn, the places correspond to scenario steps. We describe a prototype detection system consisting of an FSPN interpreter, the fuzzy ontology, and a set of predicate evaluators. Initial tests yielding promising results are reported.

The motivation for our work was an idea of integrating formal verification with business processes modeling. In the presented approach ArchiMate was selected as a language used for definition of processes. We describe a procedure, which extracts behavioral elements from ArchiMate specification and transforms them into a corresponding representation used by NuSMV model checker. Then, we focus on time efficiency of the verification task. We give results of tests performed on a set of artificial process specifications, as well as on a complex business process, whose development was supported by the implemented solution. We compare three semantics of ArchiMate process definitions and discuss their influence on model complexity and verification time.

We present recently developed solution allowing extraction of concepts from unstructured Polish texts with special focus on correct morphological forms of obtained concept names. As Polish is a highly inflected language, detected names need to be transformed following Polish grammar rules. We propose a user-friendly method for specification of transformation patterns, which is based on a simple annotations language. Annotations prepared by a user are compiled into transformation rules. During the concept extraction process the input document is split into sentences and the rules are applied to sequences of words comprised in sentences. Recognized strings forming concept names are aggregated at various levels and assigned with scores. We report also results of initial experiments performed on a medical text.

We investigate an application of model checking techniques to automated verification of business processes expressed in ArchiMate language. As a verification tool the state of the art symbolic model checker NuSMV is used. The proposed approach consists in fully automated translation of behavioral elements embedded in ArchiMate models into a corresponding representation in NuSMV language and then verifying its properties specified in CTL. Since our goal is to build an interactive verification tool, we focus on time efficiency of the verification process. We report results of tests performed on artificial process models of various complexity, as well as on a real business process example. The results show, that the described approach can be applied successfully, however, verification of complex business process specifications may face the problem of state space explosion. In such a case, to make the verification feasible, various reductions and simplifications can be applied.

Part 2: Algorithms; International audience; In this paper we investigate the problem of verification of business processes specified with ArchiMate language. The proposed solution employs model checking techniques. As a verification platform the state of the art symbolic model checker NuSMV is used.We describe a method of fully automated translation of behavioral elements embedded in ArchiMate models into a representation in NuSMV language, which is then submitted to verification with respect to requirements expressed in CTL. The requirements specification can be entered by user, but we also propose to derive some of them automatically, based on analysis of control flows within business processes. The solution was implemented as a plugin to Archi, a popular ArchiMate modeling tool. Application of the method is presented on an example of a small business process.

With the proliferation of graphics processing units (GPU) supporting general-purpose computing (GPGPU), many computationally demanding applications are being redesigned to exploit the capabilities offered by massively parallel computing platforms. This paper presents a Bees Algorithm (BA) for the Quadratic Assignment Problem (QAP) implemented on the CUDA platform. The motivations for our work were twofold: firstly, we wanted to develop a dedicated algorithm to solve the QAP showing both time and optimization performance, secondly, we planned to check if the capabilities offered by popular GPUs can be exploited to accelerate hard optimization tasks requiring high computational power. The paper describes both sequential and parallel algorithm implementations, as well as reports results of tests.

The motivation for the presented work was the need for solution allowing efficient estimation of traffic parameters in based on sparse measurements covering a small fraction road segments within large urban road network. The solution was intended to be deployed within the Dynamic Map, an important component of the INSIGMA system used for route planning and traffic control. We have chosen to base the estimation model on Fuzzy Cognitive Maps (FCM) formalism and apply an Evolutionary Algorithm to learn the model parameters. The main argument in favor of FCM is their simplicity and the speed of calculations that can be required for real-time updates of traffic parameters. This paper discusses a novel evolutionary algorithm for FCM learning and demonstrates feasibility and efficiency of such approach by giving results of tests performed on road networks of various size.

This paper presents a Particle Swarm Optimization (PSO) algorithm for the Quadratic Assignment Problem (QAP) implemented on OpenCL platform. Motivations to our work were twofold: firstly we wanted to develop a dedicated algorithm to solve the QAP showing both time and optimization performance, secondly we planned to check, if the capabilities offered by popular GPUs can be exploited to accelerate hard optimization tasks requiring high computational power. We were specifically targeting low-cost popular devices, with limited capabilities. The paper discusses the algorithm and its parallel implementation, as well as reports results of tests.

A common taxonomy of data warehouse architectures comprises five basic approaches: Centralized, Independent Data Mart, Federated, Hub-and-Spoke and Data Mart Bus. However, for many real world cases, an applied data warehouse architecture can be their combination. In this paper we propose a Data Warehouse Architecture Reference Model (DWARM), which unifies known architectural styles and provides options for adaptation to fit particular purposes of a developed data warehouse system. The model comprises 11 layers grouping containers (data stores, sources and consumers), as well as processes, covering typical functional groups: ETL, data storage, data integration and delivery. Actual data warehouse architecture can be obtained by tailoring (removing unnecessary components) and instantiating (creating required layers and components of a given type).

This paper describes DWARM, an ontology formalizing a new data warehouse architecture reference model intended do capture common five architectural approaches, as well as to provide means for describing complex hybrid architectures that emerge due to observed evolution of business and technology. The ontology defines concepts, e.g. layers, processes, containers and property classes, as well as relations that can be used to construct precise architectural models. Formalization of architecture description as an ontology gives an opportunity to perform automatic or semiautomatic validation and assessment.

We describe a prototype implementation of a real time traffic monitoring service that uses GPS positioning information received from moving vehicles to calculate average speed and travel time and assign them to road segments. The primary factor for reliability of determined parameters is the correct calculation of a vehicle location on a road segment, which is realized by a map-matching algorithm. We present an a new incremental map-matching algorithm based on Hidden Markov Model (HMM). A HMM state corresponds to a road segment and a sensor reading to an observation. The HMM model is updated on arrival of new GPS data by alternating operations: expansion and contraction. In the later step a part of determined trajectory is output. We present also results of conducted experiments.

Map-matching algorithms aim at establishing a vehicle location on a road segment based on positioning data from a variety of sensors: GPS receivers, WiFi or cellular radios. They are integral part of various Intelligent Transportation Systems (ITS) including fleet management, vehicle tracking, navigation services, traffic monitoring and congestion detection. Our work was motivated by an idea of developing an algorithm that can be both utilized for tracking individual vehicles and for monitoring traffic in real-time. We propose a new incremental map-matching algorithm that constructs of a sequence of Hidden-Markov Models (HMMs). Starting from an initial HMM, the next models are developed by alternating operations: expansion and contraction. In the later, the map-matched trace is output. We discuss results of initial experiments conducted for 20 GPS traces, which to test algorithm robustness, were modified by introduction of noise and/or downsampled.

With the growing number of vehicles traveling on public roads, traffic congestion has become a serious problem, resulting in more unpredictable travel times, increased fuel consumption and pollution. Intelligent Transportation Systems (ITS), already being developed by several countries, aim to improve safety, mobility and environmental performance. The goal of the INSIGMA project is to develop a system providing functionality of a typical ITS: real-time traffic monitoring, route planning and traffic control. In this paper we discuss the concepts and solutions developed within the project: dynamic map, sensors – videodetector and GPS tracker – as well as advanced route planning and traffic control algorithms.

We examine properties of a peer to peer network comprising several agents that store various types of local data and exchange them through established communication channels. We propose a communication model applicable to a developed platform for data integration between various security agencies and we focus on analysis of consequences of established channels, e.g. an unintended information leakage or a presence of data silos that can be an impediment for cooperation. To detect such situations efficiently, we do not concentrate on exchanged data itself, but on a belief related to known classes of data. In the analyses we use a model, in which communications and belief states are expressed as matrix operations of linear algebra. We show that applying this model we can efficiently reason about the data that can potentially be exchanged between agents not linked directly and about the ranges, which can be reached by the data during communication flows.

Automated recognition of complex video events poses challenges related to: selection of formalisms for efficient event modeling and analysis, mapping semantic high-level concepts used in specifications on information extracted from video sequences, as well as managing uncertainty associated with this information. We propose Fuzzy Semantic Petri Nets (FSPN) as a tool aimed at solving the mentioned problems. FSPN are Petri nets coupled with an underlying fuzzy ontology. The ontology stores assertions (facts) concerning object classification and detected relations. Fuzzy predicates querying the ontology are used as transition guards. Places in FSPN represent scenario steps. Tokens carry information on objects participating in a scenario and have weights expressing likelihood of a step occurrence. FSPN enable detection of events occurring concurrently, analysis of various combinations of objects and reasoning about alternatives.

In this paper we address two problems. The first pertains to implementation of medical guidelines in an e-health system supporting self-management of chronic diseases. The system allows patients to enter observed symptoms and measured parameters, then makes assessment of disease state and informs about necessary actions. We propose to formalize guidelines as sets of fuzzy rules. Fuzziness is introduced to cope with uncertainty resulting from self-observations bias, low quality of sensors and limited patients skills. The second problem is more general. It concerns the reuse of knowledge gathered in ontologies and an application of Semantic Web technologies to perform fuzzy inference. We show that, despite the fact that commonly used ontology languages and supporting tools are not intended to handle vagueness and uncertainty, they can be successfully integrated to represent and execute a set of fuzzy rules. The proposed method consists in refactoring a domain ontology, then introducing additional relations expressing fuzzy properties, encoding Mamdani fuzzy rules in SWRL language and executing them with use of Pellet OWL reasoner. We describe a fuzzy reasoning engine applying this approach and discuss translation of fuzzy rules to SWRL constructs taking as example a complete set of rules formalizing a medical guideline for asthma control assessment.

For various IT systems security is considered to be a key quality factor. In particular, for health care systems security is of uttermost importance, as it is related to patients’ health and safety. Risk assessment is an important activity in security management; it aims at identifying assets, threats and vulnerabilities, analysis of implemented countermeasures and their effectiveness in mitigating risks. This paper discusses a new risk assessment method, in which risk calculation is based on Fuzzy Cognitive Maps (FCMs) approach. FCMs are used to capture dependencies between assets and FCM based reasoning is applied to aggregate risks assigned to lower-level assets (e.g. hardware, software modules, communications, people) to such high level assets as services, maintained data and processes. An application of the method is studied on an example of e-health system providing remote telemonitoring, data storage and teleconsultation services. Lessons learned indicate, that the proposed method is an efficient and low-cost approach, giving instantaneous feedback and enabling reasoning on effectiveness of security system.

The paper reports an application of Architecture-based Tradeoff Analysis Method (ATAM) for early evaluation of the Dynamic Map architecture. The Dynamic Map is a complex information system, composed of spatial databases, storing static and dynamic data relevant for urban traffic, as well as a set of software modules responsible for data collection, interpretation and provision. Due to the complexity of the system, its size and key importance of its services to other subsystems, we decided to perform architecture evaluation using the ATAM method. To facilitate the task new tools supporting ATAM based assessment are proposed: Scenario Influence Matrix and Architectural Decision Matrix. Taking as example an excerpt from the system architecture, we present how they were used during the architecture evaluation. The gathered experience confirm usefulness of the tools, enabling ATAM to help detecting real flaws in a design and identify potential risks.

This paper presents the Dynamic Map system, one of the key products of the INSIGMA Project. The main focus is on the map and dynamic data storage subsystem, which utilizes a spatial database and is based on ontologies. First, the data models used are described, including the OpenStreetMap-based structure for the static map and the ontology-driven structure for dynamic parameters and events. The approach to generation of database structures from OWL is described in detail, followed by descriptions of the OSM import process, the GPS tracker module, the sensor state analyzer and the event interpreter. Finally, the planned future enhancements are outlined and discussed.

The paper presents IOEM, a methodology for ontology development elaborated for the INSIGMA project. Although prepared for a particular use, the methodology is quite general and can be used in a large variety of IT projects requiring ontology components. It is particularly suitable for large and geographically distributed software projects. The methodology is oriented towards applications of ontologies in various phases of a software lifecycle: development and run-time.

Use case diagrams play an important role in modeling with UML. Careful modeling is crucialin obtaining a correct and efficient system architecture. The paper refers to the formalanalysis of the use case diagrams. A formal model of use cases is proposed and its constructionfor typical relationships between use cases is described. Two methods of formal analysis andverification are presented. The first one based on a states’ exploration represents a modelchecking approach. The second one refers to the symbolic reasoning using formal methodsof temporal logic. Simple but representative example of the use case scenario verification isdiscussed.

LACATRE is a graphical language intended to help in design for applications requiring multitasking real time executives. Within the software life cycle, it is used for both preliminary and detailed design. The paper presents new high level objects destined to facilitate the link between commonly used methods for specification and design and the detailed design using multitasking executives. The paper describes ideas concerning the use of such a tool in the frame of education, and industry in the case of medium size applications where developers have a lack of tools. >

For many classes of problems it is difficult to make decisions, assessments or develop plans based on precise quantitative models. Fuzzy Cognitive Maps (FCM) are a well-known tool for qualitative analysis of systems that uses a simple representation of knowledge in the form of a graph of concepts linked by causal relationships. Advantages of FCMs are the ease of gathering and representing knowledge and the simplicity of reasoning techniques, very close to neural networks. The paper gives results of experiments aiming at application of FCMs to analysis of development scenarios for research and teaching units. The analyzes were conducted for four representative classes of academic units: strong, medium, weak with a development potential and weak; their results are discussed and several observations related to reasoning with FCMs are made.

SIMPOZ project aims at building a highly configurable system for surveillance of public areas and objects of a special importance based on the analysis of digital images. Distributed nature and potential diversity of monitoring features, a large number of alarm signals fed by various sensors, the need to ensure a flexible event flow management and tailored to needs emergency response processes were motivations to utilize workflow as a management and integration layer. Implementation of workflow processes was preceded by business modeling with ArchiMate language. The presented models of business processes reflect the specificity and characteristics of surveillance systems: reactive character, event driven control and a large number of asynchronous data flows. Lessons learned indicate, that ArchiMate models in spite of the smaller set of dedicated behavioral constructs with respect to for example BPMN, after adopting certain modeling conventions, allow to reach high level of expressiveness.

Dynamic Map can be considered a complex information system, composed of spatial databases, storing static and dynamic data relevant for urban traffic, as well as a set of software modules responsible for data collection, interpretation and provision. The data originates from a network of sensors, both fixed (e.g. video detectors, acoustic sensors, inductive loops) and on-board GPS receivers installed in vehicles. Clients of the Dynamic Map are various software modules performing such tasks as visualization, route planning, traffic optimization, object tracking and threat detection. The paper discusses experiences with an evaluation of the system architecture using the ATAM (Architectural Tradeoffs Analysis Method). The stages of evaluation comprise describing architectural views, identification of key scenarios and evaluation of risks of their realization.