The paper focuses on the design and development of a two-wheeled and self-balancing robot as well as its control. The problem is equivalent to the inverted pendulum principle of balancing robots. Dynamic model based PD controller and empirical controller were designed. These controllers are used in closed loop system to provide the robot balance, even when robot is slightly pushed, which normally causes it to fall. The equations of robot motion were derived using Lagrangian and mapped to a transfer function in the complex s-domain. The controller parameters are initially tuned using PID Tuner in Simulink. Then, the zero-order-hold discretization method was applied to implement this control on the Arduino microcontroller. Furthermore, the controller parameters are additionally adjusted through experiments in order to exhibit better control performance. Moreover, the effect of an unexpected disturbance on the robot was taken into consideration. The effectiveness of the designed controllers was verified experimentally.

Abstract. Alongside climate change, the introduction of non-native species (NNS) is widely recognized as one of the main threats to aquatic biodiversity and human wellbeing. Non-native species and biodiversity are generally low priority issues on the political agendas of many countries, particularly in European countries outside the European Union (EU). The objectives and tasks of this study were to address the policy regulation, education level, education practices, and socioeconomic perceptions of NNS in the Balkans. A questionnaire-based survey was conducted in Albania, Bosnia and Herzegovina, Montenegro, North Macedonia and Turkey (Balkan EU candidate and potential candidate members), in Croatia and Greece (Balkan EU Member States) and Italy (non-Balkan EU Member State). The EU Alien Regulation (1143/2014) concerning NNS is implemented in EU Member States and Montenegro, whereas Albania, Bosnia and Herzegovina and Turkey have not reported specific policy regulations for NNS. Permanent monitoring programmes specifically designed for NNS have not yet been established in the EU Member States. Most countries tackle the issue of NNS through educational activities as part of specific projects. Education level is indicative of the implementation of NNS policy regulation, and efforts are needed for the proper development of relative study programmes. Public awareness and educational preparedness concerning NNS in the Balkans were identified as poor. Strong programmes for management and education should be developed to increase public awareness to prevent further biodiversity losses in the Balkan region.

The goal of this article is to describe a power measuring system using the frequency-locked-loop (FLL). The FLLs have a wide variety of applications such as power converters, grid synchronization, sensorless flux estimation and control of motor drives. This nature of the FLL system allows for it to be a potentially perfect tool for power calculation. In this article, a new power calculation method has been presented. This method is based on FLL as part of phase locked loop (PLL) and has enhanced feature over classical methods for power calculation widely used in industry. The obtained results showcase the effectiveness of the proposed FLL power calculation method.

This paper is focused on investigating a new approach for collecting the data about robot’s position and orientation from the RoboDK software in which the robot’s movement is simulated. The code, as well as the GUI (Graphical User Interface) was developed using MATLAB environment and connection between the two software packages was achieved through the TCP (Transmission Control Protocol) internet protocol. Robot model used for this purpose is the Unimation’s PUMA 560 industrial robot which can be found in the online library of the RoboDK. Information regarding robot joint angles is important for planning the robot’s movement and generating the trajectory between the two positions, so it could be of use to have all the position/orientation vectors listed for further calculations. Drawback of the RoboDK software is the inability to generate the trajectory so the goal of this project is to avoid that problem with the help of the MATLAB software.

Sex assessment is an important step of the forensic process. Dental remains are often the only remains left to examine due to their resistance to decay and external factors. Contemporary forensic odontology literature describes multiple methods for sex assessment from mandibular parameters, all of which require manual measurements and expert training. This study aims to explore the applicability of deep learning and image analysis methods to automate this task, thus allowing for easier reproducibility of assessments, reduction of the time experts lose on repetitive tasks, and potentially better performance. We have evaluated state-of-the-art deep learning models and components on the largest dataset of individual adult tooth x-ray images, consisting of 76293 samples. This study also explores the usage of decayed or structurally altered teeth, with which contemporary methods struggle. Two types of models are constructed, a family of models specialized for specific tooth types, and a general model that can assess the sex from any tooth type. We examine the performance of those models per tooth type and age group, as well as the impact of decayed and structurally altered teeth. The specialized models achieve an overall accuracy of 72.40%, and the general model reaches an overall accuracy of 72.68%.

The OPENQKD project is demonstrating deployed QKD networks in several European cities. We present a virtual QKD testbed that allows the user to monitor live data but also to re-enact the past QKD exchange over the various links, together with a QKD network simulation tool.

Connecting a photovoltaic solar power plant (PVSP) to a radial low-voltage network can significantly influence the power quality. According to grid rules in our country, the PVSP with rated power of up to 150 kW can be connected to the low-voltage power distribution network. Common case is that the PVSP with rated power of approximately 100 kW is installed at the rooftop of a residential or commercial building and connected to one connection point. Usually, the owner tends to regulate PVSP to operate at unity power factor to maximize profit. However, when the power plant operates with unity power factor, voltages at the connection point and at the nearby buses are often increased. Also, a significant increase of power losses can occur. This paper describes the procedure for determining the maximum power of the PVSP operating with unity power factor while simultaneously meeting the following criteria: (1) the range of slow voltage variations must be within the limits defined by the EN 50160 standard; (2) after connecting the PVSP, the active power losses should be less or equal to active power losses calculated with the disconnected PVSP.

Acute myocardial infarction (AMI) and the heart failure (HF) which may follow are among the leading causes of death and disability worldwide. As such, new therapeutic interventions are still needed to protect the heart against acute ischemia/reperfusion injury to reduce myocardial infarct size and prevent the onset of HF in patients presenting with AMI. However, the clinical translation of cardioprotective interventions that have proven to be beneficial in preclinical animal studies, has been challenging. One likely major reason for this failure to translate cardioprotection into patient benefit is the lack of rigorous and systematic in vivo preclinical assessment of the efficacy of promising cardioprotective interventions prior to their clinical evaluation. To address this, we propose an in vivo set of step-by-step criteria for IM proving P reclinical A ssessment of C ardioprotective T herapies (‘IMPACT’), for investigators to consider adopting before embarking on clinical studies, the aim of which is to improve the likelihood of translating novel cardioprotective interventions into the clinical setting for patient benefit.

The experimental investigation of Newton’s law of universal gravitation requires expensive and sensitive equipment. Therefore, it is often not possible to conduct such an experiment within a typical physics class. An alternative is to investigate the universal gravitation law by using simulations. In this paper, we are presenting such an augmented reality simulation (GravityAR). The simulation has been developed for mobile devices and runs on an Android operating system, making it very accessible for students and teachers at all educational levels. It allows the user to investigate how the gravitational force between two spherical bodies depends on their masses and mutual distance. Additionally, the simulation can be used to determine the value of the gravitational constant (G).

Two-wave with diffuse power (TWDP) is one of the most promising distribution for description of a small-scale fading in the emerging mmWave band. However, traditional error performance analysis in these channels faces two fundamental issues. It is mostly based on conventional TWDP parameterization which is not in accordance with the model’s underlying physical mechanisms and which hinders accurate observation of the impact of a model parameters on a system’s performance metrics. In addition, the existing average bit/symbol error probability (ABEP/ASEP) expressions for most modulations and diversity schemes are available as approximations, which are accurate only for specific combinations of TWDP parameters. Accordingly, in this paper, the exact ASEP expressions are derived for M-ary rectangular quadrature amplitude modulation (RQAM) with coherent detection and for M-ary DPSK modulation, and are given in terms of physically justified parameters. Besides, in order to relax computational complexity of proposed exact ASEPs in high signal-to-noise ratio (SNR) region, their asymptotic counterparts are derived as the simple closed-form expressions, matching the exact ones for SNR>30dB. Results are verified by Monte-Carlo simulation.

In the offshore industry, unmanned autonomous systems are expected to have a permanent role in future operations. During offshore operations, the unmanned autonomous system needs definite instructions on evaluating the gathered data to make decisions and react in real-time when the situation requires it. We rely on video surveillance and sensor measurements to recognize early warning signals of a failing asset during the autonomous operation. Missing out on the warning signals can lead to a catastrophic impact on the environment and a significant financial loss. This research is helping to solve the issue of trustworthiness of the algorithms that enable autonomy by capturing the rising risks when machine learning unintentionally fails. Previous studies demonstrate that understanding machine learning algorithms, finding patterns in anomalies, and calibrating trust can promote the system’s reliability. Existing approaches focus on improving the machine learning algorithms and understanding the shortcomings in the data collection. However, recollecting the data is often an expensive and extensive task. By transferring knowledge from multiple disciplines, diverse approaches will be observed to capture the risk and calibrate the trust in autonomous systems. This research proposes a conceptual framework that captures the known risks and creates a safety net around the autonomy-enabling algorithms to improve the reliability of the autonomous operations.

Climate problems, the increasingly robust European emissions policy and falling prices of solar and wind have led to the shutdown of many thermal power plants and increased installation of renewable energy power plants. The installed capacity of wind and solar power plants in our country is not yet significant, but small hydropower plants are often considered as a good solution for the power supply to remote areas despite the problems that sometimes arise after the installation of these power plants. This paper describes a power quality problem of high voltages occurs after the installation of a small hydropower plant of 800 kVA in an area with predominantly industrial consumers. Many industries use high technology for manufacturing and require high power quality and reliability of power supply. Even modest power quality problems can have significant technical and economic effects on these consumers. Therefore, it was necessary to precisely monitor power quality to adequately address all related problem recorded during the plant performance test phase. Also, the observed problem has led to power plant outage on several occasions in a short period of time which resulted in a reduction in power generation. Techniques for mitigation the observed power quality issue are also considered in this paper. The implemented solution of the problem is verified by power quality monitoring and SCADA system measurements.