A connection between formation infeasibility and velocity alignment in kinematic multi-agent systems

In this paper, a feedback control strategy that achieves convergence of a multi-agent system to a desired formation configuration is proposed for both the cases of agents with single integrator and nonholonomic unicycle-type kinematics. When inter-agent objectives that specify the desired formation cannot occur simultaneously in the state space the desired formation is infeasible. It is shown that under certain assumptions, formation infeasibility forces the agents’ velocity vectors to a common value at steady state. This provides a connection between formation infeasibility and flocking behavior for the multi-agent system. We finally also obtain an analytic expression of the common velocity vector in the case of formation infeasibility.     

Two efficient solutions for visual odometry using directional correspondence

This paper presents two new, efficient solutions to the two-view, relative pose problem from three image point correspondences and one common reference direction. This three-plus-one problem can be used either as a substitute for the classic five-point algorithm, using a vanishing point for the reference direction, or to make use of an inertial measurement unit commonly available on robots and mobile devices where the gravity vector becomes the reference direction. We provide a simple, closed-form solution and a solution based on algebraic geometry which offers numerical advantages. In addition, we introduce a new method for computing visual odometry with RANSAC and four point correspondences per hypothesis. In a set of real experiments, we demonstrate the power of our approach by comparing it to the five-point method in a hypothesize-and-test visual odometry setting.     

Inflammation in Coronary Microvascular Dysfunction

Chronic low-grade inflammation is involved in coronary atherosclerosis, presenting multiple clinical manifestations ranging from asymptomatic to stable angina, acute coronary syndrome, heart failure and sudden cardiac death. Coronary microvasculature consists of vessels with a diameter less than 500 μm, whose potential structural and functional abnormalities can lead to inappropriate dilatation and an inability to meet the required myocardium oxygen demands. This review focuses on the pathogenesis of coronary microvascular dysfunction and the capability of non-invasive screening methods to detect the phenomenon. Anti-inflammatory agents, such as statins and immunomodulators, including anakinra, tocilizumab, and tumor necrosis factor-alpha inhibitors, have been assessed recently and may constitute additional or alternative treatment approaches to reduce cardiovascular events in atherosclerotic heart disease characterized by coronary microvascular dysfunction.     

Effects of varying levels of dietary free gossypol in whole cotton seed on physiological responses of growing–fattening lambs

Effects of increasing concentrations of free gossypol in diets containing up to 30% whole cottonseed on blood metabolites were studied using 32 Karagouniko male lambs in a 4 × 3 factorial arrangement with treatments of four whole cottonseed levels and three times of sampling. The diets used were isocaloric (12.5–13.1 MJ metabolisable energy kg⁻¹ dry matter) with 163–186 g crude protein kg⁻¹ dry matter containing 0%, 5–10%, 10–20% and 15–30% whole cottonseed and were fed ad libitum for 54 days. Blood samples were taken at the beginning, middle and end of the experiment. The blood parameters examined were: haematocrit, haemoglobin, mean corpuscular haemoglobin concentration, erythrocyte sedimentation rate, plasma glucose, urea, total protein, albumin and globulin concentrations and serum total and free cholesterol, cholesterol esters and inorganic K concentrations. In addition, the serum enzyme activities of glutamate pyruvate transaminase, γ-glutamyl transpeptidase and lactate dehydrogenase, as indicative of liver and heart muscle damage, and the concentrations of the major elements Ca, Mg and K and also of the trace elements Fe, Cu and Zn in liver, kidneys and heart of slaughtered lambs were estimated. This study was also made to determine the effect of whole cottonseed diets on the accumulation of free gossypol in some organs of growing lambs. Most of the blood components evaluated were affected by the diet fed. Blood haematocrit and erythrocyte sedimentation rate were higher at the beginning of the experiment, but haemoglobin and mean corpuscular haemoglobin concentrations were higher at day 28 of the experimental period; plasma total protein, urea, albumin and globulin concentrations and also serum total and free cholesterol and inorganic K concentrations were higher at day 54 of the experiment. The serum enzyme activities of glutamate pyruvate transaminase, γ-glutamyl transpeptidase and lactate dehydrogenase were all higher at day 54 of the experimental period. However, there were significant differences between treatments and between times of sampling in the three enzyme activities measured. Liver, kidneys and heart examination showed significant differences for all organs in NPN, fat, fat/protein ratio and the major elements Ca, Mg and K among the four treatments. However, of the trace elements examined, differences were significant for Fe in liver, Cu in heart and Zn in liver and heart. The greatest free gossypol accumulation was observed, as expected, in liver, followed by kidneys and lastly by heart. There was an indication of possible gossypol toxicity in these growing lambs as a result of the marked elevations of the activity of lactate dehydrogenase and possibly of the increase in plasma urea concentration. © 1999 Society of Chemical Industry     

Sparse episode identification in environmental datasets: The case of air quality assessment

Sparse episode identification in environmental datasets is not only a multi-faceted and computationally challenging problem for machine learning algorithms, but also a difficult task for human-decision makers: the strict regulatory framework, in combination with the public demand for better information services, poses the need for robust, efficient and, more importantly, understandable forecasting models. Additionally, these models need to provide decision-makers with “summarized” and valuable knowledge, that has to be subjected to a thorough evaluation procedure, easily translated to services and/or actions in actual decision making situations, and integratable with existing Environmental Management Systems (EMSs).On this basis, our current study investigates the potential of various machine learning algorithms as tools for air quality (AQ) episode forecasting and assesses them – given the corresponding domain-specific requirements – using an evaluation procedure, tailored to the task at hand. Among the algorithms employed in the experimental phase, our main focus is on ZCS-DM, an evolutionary rule-induction algorithm specifically designed to tackle this class of problems – that is classification problems with skewed class distributions, where cost-sensitive model building is required.Overall, we consider this investigation successful, in terms of its aforementioned goals and constraints: obtained experimental results reveal the potential of rule-based algorithms for urban AQ forecasting, and point towards ZCS-DM as the most suitable algorithm for the target domain, providing the best trade-off between model performance and understandability.     

Precipitation Forecast Using Artificial Neural Networks in Specific Regions of Greece

In recent years, significant changes in precipitation regimes have been observed and these manifest in socio economic and ecological problems especially in regions with increased vulnerability such as the Mediterranean region. For this reason, it is necessary to estimate the future projected precipitation on short and long-term basis by analyzing long time series of observed station data. In this study, an effort was made in order to forecast the monthly maximum, minimum, mean and cumulative precipitation totals within a period of the next four consecutive months, using Artificial Neural Networks (ANNs). The precipitation datasets concern monthly totals recorded at four meteorological stations (Alexandroupolis, Thessaloniki, Athens, and Patras), in Greece. For the evaluation of the results and the ability of the developed prognostic models, appropriate statistical indexes such as the coefficient of determination (R ²), the index of agreement (IA) and the root mean square error (RMSE) were used. The findings from this analysis showed that the ANN’s methodology provides satisfactory precipitation totals in four consecutive months and these results are better results, than those obtained using classical statistical methods. A fairly good consistency between the observed and the predicted precipitation totals at a statistical significance level of p < 0.01 for the most of the examined cases has been revealed. More specifically, the Index of Agreement (IA) ranges between 0.523 and 0.867 and the coefficient of determination (R ²) ranges between 0.141 and 0.603. The most accurate forecasts concern the mean monthly and the cumulative precipitation for the next four consecutive months.     

Departmental efficiency differences within a Greek university: An application of a DEA and Tobit analysis

The allocation funds mechanisms for universities and their departments seem to be a complex procedure for state authorities. Consequently, the question of how efficient universities and their departments are has been imposed by governments and policymakers. In this paper, we assess research performance of academic departments within a single Greek university. A Data Envelopment Analysis (DEA) application with six model variants was used to estimate technical efficiencies relative to best practice performance. Furthermore, our input–output criteria are determined and used to measure the departments' academic performance. To deliver reasonable results a Tobit model is considered in a second stage to examine the degree to which “environmental effects” impact on departmental efficiencies. We find that “environmental effects” such as departmental infrastructure, age and schools' personnel have an important role.     

Constrained and Unconstrained overspill routing in optical networks: a detailed performance evaluation

In this article, we present a detailed performance evaluation of a hybrid optical switching (HOS) architecture called Overspill Routing in Optical Networks (ORION). The ORION architecture combines (optical) wavelength and (electronic) packet switching, so as to obtain the individual advantages of both switching paradigms. In particular, ORION exploits the possible idle periods of established lightpaths to transmit packets destined to the next common node, or even directly to their common end-destination. Depending on whether all lightpaths are allowed to simultaneously carry and terminate overspill traffic or overspill is restricted to a sub-set of wavelengths, the architecture limits itself to constrained or un-constrained ORION. To evaluate both cases, we developed an extensive network simulator where the basic features of the ORION architecture were modeled, including suitable edge/core node switches and load-varying sources to simulate overloading traffic conditions. Further, we have assessed various aspects of the ORION architecture including two basic routing/forwarding policies and various buffering schemes. The complete network study shows that ORION can absorb temporal traffic overloads, as intended, provided sufficient buffering is present. We also demonstrate that the restriction of simultaneous packet insertions/extractions, to reduce the necessary interfaces, do not deteriorate performance and thus the use of traffic concentrators assure ORION’s economic viability.     

Geographical classification of Greek virgin olive oil by non-parametric multivariate evaluation of fatty acid composition

Fatty acid compositional data for Greek virgin olive oils from 24 years of harvest and various regions and cultivars were evaluated using chemometric methods. Non-parametric discriminant analysis after proper transformation of the data seems to be a suitable approach to characterise the oils according to the geographical origin and may produce a scientific basis for the assignment of an ‘appellation d'origine’ trade mark.