The article presents solutions to anomaly detection in network traffic for critical smart metering infrastructure, realized with the use of radio sensory network. The structure of the examined smart meter network and the key security aspects which have influence on the correct performance of an advanced metering infrastructure (possibility of passive and active cyberattacks) are described. An effective and quick anomaly detection method is proposed. At its initial stage, Cook’s distance was used for detection and elimination of outlier observations. So prepared data was used to estimate standard statistical models based on exponential smoothing, that is, Brown’s, Holt’s, and Winters’ models. To estimate possible fluctuations in forecasts of the implemented models, properly parameterized Bollinger Bands was used. Next, statistical relations between the estimated traffic model and its real variability were examined to detect abnormal behavior, which could indicate a cyberattack attempt. An update procedure of standard models in case there were significant real network traffic fluctuations was also proposed. The choice of optimal parameter values of statistical models was realized as forecast error minimization. The results confirmed efficiency of the presented method and accuracy of choice of the proper statistical model for the analyzed time series.

In this article Tomasz Andrysiak presents the use of sparse representation of a signal based on overcomplete dictionaries of base functions and a QRS detection method using artificial neural network to detect anomalies in the analyzed ECG signals. Performance of the proposed method was tested by means of a widely available database of ECG signals, i.e., the MIT-BIH Arrhythmia Database, and the obtained experimental results confirmed its effectiveness for anomaly detection in the analyzed ECG signals. The current dynamic and intensive development of information technologies and more excellent methods of processing, analysis, and recognition of signals have enriched medicine with new quality techniques in diagnosis and therapy. At present, a special interest is put to noninvasive diagnostic methods for rapid and objective determination of vital signs and, in particular, automatic electrocardiogram performed outside medical facilities. The sparse representation of a signal was performed in an adaptive manner by means of the matching pursuit algorithm. In each step of the algorithm, there was implemented a linear signal decomposition of features belonging to the dictionary with redundancy. Two kinds of dictionaries were tested. The first type included elements that form the analytical base functions, and the second represented the essential components of the ECG signal searched with the use of the K-Singular Value Decomposition (K-SVD) algorithm. Recognition of the QRS was realized by means of a unidirectional, multidimensional neural network with backpropagation of an error. For this solution, tests were performed on a selection of appropriate neural network architectures and the impact of a particular activation function on the recognition results. Anomaly detection was realized with estimation of sparse representation parameters of the tested ECG signal within the field of the recognized QRS complex, and it was compared with the reference values. Performance of the proposed method was tested using a widely available database of ECG signals—MIT-BIH Arrhythmia Database. The obtained experimental results confirmed the effectiveness of the proposed method of anomaly detection in the analyzed ECG signals.

Lubrication of articular cartilage is a complex multiscale phenomenon in synovial joint organ systems. In these systems, synovial fluid properties result from synergistic interactions between a variety of molecular constituent. Two molecular classes in particular are of importance in understanding lubrication mechanisms: hyaluronic acid and phospholipids. The purpose of this study is to evaluate interactions between hyaluronic acid and phospholipids at various functionality levels during normal and pathological synovial fluid conditions. Molecular dynamic simulations of hyaluronic acid and phospholipids complexes were performed with the concentration of hyaluronic acid set at a constant value for two organizational forms, extended (normal) and coiled (pathologic). The results demonstrated that phospholipids affect the crosslinking mechanisms of hyaluronic acid significantly and the influence is higher during pathological conditions. During normal conditions, hyaluronic acid and phospholipid interactions seem to have no competing mechanism to that of the interaction between hyaluronic acid to hyaluronic acid. On the other hand, the structures formed under pathologic conditions were highly affected by phospholipid concentration.

Glycosaminoglycans are a wide class of biopolymers showing great lubricating properties due to their structure and high affinity to water. Two of them, hyaluronic acid and chondroitin sulfate, play an important role in articular cartilage lubrication. In this work, we present results of the all-atom molecular dynamics simulations of both molecules placed in water-based solution. To mimic changes of the physiological conditions, especially temperature, of the synovial fluid in joints under successive load (e.g., walking, jogging, jumping), simulations have been performed at different physiological temperatures in the range of 300 to 320 Kelvin (normal intra-articular temperature is 305 K). The stability of the biopolymeric network at equilibrium (isothermal and isobaric) conditions has been studied. To understand the process of physical crosslinking, the dynamics of intra- and intermolecular hydrogen bonds forming and breaking have been studied. The results show that following addition of chondroitin sulfate, hyaluronan creates more intermolecular hydrogen bonds than when in homogeneous solution. The presence of chondroitin in a hyaluronan network is beneficial as it may increase its stability. Presented data show hyaluronic acid and chondroitin sulfate as viscosity modifiers related to their crosslinking properties in different physicochemical conditions.