In real datasets often occur cases, where variable or multiple variables have unusual values. These cases are known as anomalies or outliers. For any analysis, it is essential to detect them, because they can bias the analysis. In this paper, a robust anomaly detection method is presented, and it is based on median, rather then on mean value. The method is explained, as well as its parameters and the way how they affect the results. The method is then implemented, and used on Internal Banking Payment Systems. Analysis is given and results are presented.

The class of even-hole-free graphs is very similar to the class of perfect graphs, and was indeed a cornerstone in the tools leading to the proof of the Strong Perfect Graph Theorem. However, the complexity of computing a maximum independent set (MIS) is a long-standing open question in even-hole-free graphs. From the hardness point of view, MIS is W[1]-hard in the class of graphs without induced 4-cycle (when parameterized by the solution size). Halfway of these, we show in this paper that MIS is FPT when parameterized by the solution size in the class of even-hole-free graphs. The main idea is to apply twice the well-known technique of augmenting graphs to extend some initial independent set.

Many companies own a significant number of vehicles. To ensure the undisturbed company workflow, all vehicles have to be tracked. The standard way of vehicle tracking is via a GPS device. Sometimes, GPS devices are sending fallacious data to the server. That data can cause significant errors in daily reports or in the vehicle route preview. This paper describes an efficient technique for finding different types of anomalies in GPS data. The paper describes a connection between finding a QRS complex in ECG signal and anomalies in GPS data. The algorithm is implemented and used as a part of the GPS tracking system that is used by distribution companies in Bosnia and Herzegovina.

Depending on actual load profiles connected to grid containing a PV system, losses and power quality disturbances vary during the day due to the power unbalance in the connection node. With the increase in size of PV power plants this problem becomes more important. Different load profiles have different correlation with the daily power generation from the PV system. Therefore, economic and technical impact of different daily load curves on grid connected PV systems should be considered. This paper gives an analysis of aforementioned problems. After the description and comparison between different load profiles and daily load curves, a simulation model is described and different situations of occurring problems are tested and analyzed. Simulations were carried out with real load profiles. Finally, this paper gives an overview of problems and gives few proposes for their solution.

Abstract The paper first investigates the influence of daily mobility of population on evaluation of aircraft noise effects. Then, a new air traffic assignment model that considers this activity is proposed. The main objective is to reduce the number of people affected by noise via lowering as much as possible the noise exposure level Lden of individuals or groups of people who commute to the same locations during the day. It is hereby intended to reduce the noise impact upon individuals rather than to reduce the impact in particular – typically densely populated – areas. However, sending aircraft farther away from populated regions to reduce noise impact may increase fuel burn, thus affecting airline costs and sustainability. Therefore, a multi-objective optimization approach is utilized to obtain reasonable solutions that comply with overall air transport sustainability. The method aims at generating a set of solutions that provide proper balance between noise annoyance and fuel consumption. The reliability and applicability of the proposed method are validated through a real case study at Belgrade airport in Serbia. The investigation shows that there is a difference between the number of people annoyed (NPA) evaluated based on the census data and the NPA evaluated based on the mobility data. In addition, these numbers differ significantly across residential locations. The optimal results show that the proposed model can offer a considerable reduction in the NPA, and in some cases, it can gain up to 77%, while maintaining the same level of fuel consumption compared with the reference case.

This paper presents a new approach for solving decentralized bi-level multi-objective linear fractional programming problems. The main goal was to find a simple algorithm with high confidence of decision-makers in the results. First, all the linear fractional programming models on the given set of constraints were solved separately. Next, all the linear fractional objective functions were linearized, membership functions of objective functions and decision variables controlled by decision-makers at the highest level calculated, and a fuzzy multi-objective linear programming model formed and solved as linear goal programming problem by using simplex algorithm. The efficiency of the proposed algorithm was investigated using an economic example, and the obtained results compared with those obtained using an existing method.

Increasing student motivation and engagement in classroom (and during the study in general) is the aim of every lecturer. Never stopping development of new digital tools and media present a new challenge in the educational process. The goal of this research is to increase the knowledge and understanding of the influence of Bring Your Own Device (BYOD) approach (and use of the mobile devices in classrooms in general) on: teachers’ practice and students’ classroom activities, students’ attitude about bringing the mobile phones in the class and mobile phone applications in education processes. This research focuses on undergraduate and postgraduate mechanical engineering students. Personal reflection of the lecturers and online survey for students was used as a tool to investigate participants’ attitude towards mobile applications as a method of promotion of active learning in engineering education.

Burst suppression includes alternating patterns of silent and fast spike activities in neuronal activities observable (in micro or macro scale) electro-physiological recordings. Biological models of burst suppression are given as dynamical systems with slow and fast states. The aim of this paper is to give a method to identify parameters of a mesoscopic model of burst suppression that can provide insights into study underlying generators of intracranial electroencephalogram (iEEG) data. An optimisation technique based upon a genetic algorithm (GA) is employed to find feasible model parameters to replicate burst patterns in the iEEG data with paroxysmal transitions. Then, a continuous-discrete unscented Kalman filter (CD-UKF) is used to infer hidden states of the model and to enhance the identification results from the GA. The results show promise in finding the model parameters of a partially observed mesoscopic model of burst suppression.

Epileptic seizures may be initiated by random neuronal fluctuations and/or by pathological slow regulatory dynamics of ion currents. This paper presents extensions to the Jansen and Rit neural mass model (JRNMM) to replicate paroxysmal transitions in intracranial electroencephalogram (iEEG) recordings. First, the Duffing NMM (DNMM) is introduced to emulate stochastic generators of seizures. The DNMM is constructed by applying perturbations to linear models of synaptic transmission in each neural population of the JRNMM. Then, the slow-fast DNMM is introduced by considering slow dynamics (relative to membrane potential and firing rate) of some internal parameters of the DNMM to replicate pathological evolution of ion currents. Through simulation, it is illustrated that the slow-fast DNMM exhibits transitions to and from seizures with etiologies that are linked either to random input fluctuations or pathological evolution of slow states. Estimation and optimization of a log likelihood function (LLF) using a continuous-discrete unscented Kalman filter (CD-UKF) and a genetic algorithm (GA) are performed to capture dynamics of iEEG data with paroxysmal transitions.

Abstract The present work deals with analysis of instabilities in DCMIX1 ternary systems in the experimental geometry when two liquid layers with different concentrations are superimposed. In multicomponent mixtures, depending on the initial conditions, it is possible to have convective motion even when the top part is less dense than the bottom one. This unstable behavior destroys boundary interfaces unpredictably and affects diffusion processes. Such behavior was examined considering as multicomponent mixture the DCMIX1 ternary system. Due to its three different constituents, the mixture could easily generate two possible types of instability regimes such as: fingers and overstability. Five different compositions have been initially selected, three around the fingers-like type region and two around the overstability region. Experimental results obtained during Sliding Symmetric Tubes (SST) technique based experiments have been compared with the analytical pure diffusion solution and also with a 3D numerical simulation which includes the buoyancy effects. For this purpose, a specific solver in the open source software OpenFOAM has been created. Predicted instabilities agree well with experimental results in most of the cases, confirming the accuracy of the obtained diffusion coefficients.

The 16 articles in this special section examine both licensed and unlicensed spectrum for 5G/B5G wireless networks. The incredible increase in connected appliances and downloaded applications has pushed mobile operators to the limits of their licensed spectrum bands. This has triggered the idea of evolving the current radio access network to use the underutilized unlicensed spectrum to extend spectrum resources beyond current usage charts. This mode of cellular access has raised a lot of questions about use cases, enabling technologies, and fairness to other native unlicensed users, such as WiFi. Nevertheless, unlicensed access is being accepted as one of the most significant solutions to improve the resource availability and system scalability in future fifth generation (5G)/beyond 5G (B5G) networks.

Hyoid bone movement is an important physiological event during swallowing that contributes to normal swallowing function. In order to determine the adequate hyoid bone movement, clinicians conduct an X-ray videofluoroscopic swallowing study, which even though it is the gold-standard technique, has limitations such as radiation exposure and cost. Here, we demonstrated the ability to track the hyoid bone movement using a non-invasive accelerometry sensor attached to the surface of the human neck. Specifically, deep neural networks were used to mathematically describe the relationship between hyoid bone movement and sensor signals. Training and validation of the system were conducted on a dataset of 400 swallows from 114 patients. Our experiments indicated the computer-aided hyoid bone movement prediction has a promising performance when compared with human experts’ judgements, revealing that the universal pattern of the hyoid bone movement is acquirable by the highly nonlinear algorithm. Such a sensor-supported strategy offers an alternative and widely available method for online hyoid bone movement tracking without any radiation side-effects and provides a pronounced and flexible approach for identifying dysphagia and other swallowing disorders.

This paper presents a method for distributed generation (DG) allocation in low voltage distribution network based on the total annual energy loss reduction and Artificial Neural Network (ANN). The proposed method is applied to the PV solar based DG allocation problem in the low voltage distribution network using realistic network data and measurements. This research is motivated by numerous realistic issues faced by the Distribution System Operator in the area of DG planning. The main objective of this work is to develop, test and validate a robust method for DG allocation which can be used in practical problems without the need for extensive system modelling and load flow analysis. The results confirm the importance of appropriate DG planning and show that the proposed method can be used as a promising tool for efficient and effective DG allocation in low voltage distribution network.