An asset administration shell (AAS), as a key concept of the Industry 4.0, provides a machine-accessible interface to any kind of asset. An application implementing the devices functionality should be able to interact with different AASs.In this work, we specify the function blocks (FBs) for accessing properties and invoking operations of AASs. We analyzed the nature and requirements of such FBs while focusing on REST/HTTP- and OPC UA-based AASs and provided IEC 61499-based implementation.The results obtained in this paper will ease interaction with the complex AAS structure from the low-level devices.

This paper presents a design of the mobile robot motion framework using slightly modified Vector Field Histogram (VFH) algorithm. The VFH algorithm provides both local motion planning and obstacle avoidance based on on-board sensors measurements. This framework utilizes Robot Operating System (ROS) for the software implementation and simulation purposes. The effectiveness of the proposed framework was verified in both static and dynamic unknown environments. The obtained simulation results indicate a proficiency of the VFH algorithm in navigating the mobile robot from the start to the goal position avoiding collision with obstacles. Trajectories obtained using the designed framework are smooth and without oscillations even in the presence of moving obstacles.

This paper treats a path planning problem for the mobile robot with differential constraints using modified RRT (Rapidly exploring random tree) algorithm based on Dubin’s curves. In this paper, the planning problem is considered as a problem of finding a feasible path between the initial and goal point in a static environment with obstacles. Modifications on basic RRT algorithm are necessary due to differential constraints of non-holonomic car-like robot. Dubin’s vehicle is selected as robot model, which represents special case of Simple car that moves only forward with constant velocity and can only make left and right turns. Algorithm is implemented in ROS (Robot Operating System) and tested through realistic simulations in 2D environment consisted of free space and obstacles. The simulation setup was conducted using a 2D Stage simulator in ROS and RViz tool for visualization. Pioneer 3AT robot model was used as Dubin’s car for simulation purposes.

Abstract Paper deals with seasonal changes in heavy metal bioaccumulation (Fe, Mn, Cu, Zn and Pb) in Utricularia vulgaris L. and Salvinia natans (L.) All. of two localities (Necik and Sinjak – active fishpond basins) in the area of Bardača fishpond. According to our results, the better accumulator of Fe (3035 mg/kg) and Zn was S. natans (163.55 mg/kg), whereas Utricularia vulgaris better accumulates Mn (620 mg/kg) and Cu (10.18 mg/kg). Amounts of Pb in both investigated macrophyte were below the detection level (<0.51 mg/kg). The values of the bioaccumulation factor (BAC) of the tested metals were >1 or ~1 for both species, and the BAC values decreased in the following order in both investigated species: Fe > Mn > Cu > Zn. The results obtained indicated that tested macrophyte show very good characteristics as bioaccumulators and, thanks to this fact, they could be used in phytoremediation technique successfully in water-polluted ecosystem.

Analysis of the Temple Scroll reveals another technology used to produce the Dead Sea Scrolls and potential preservation concerns. The miraculously preserved 2000-year-old Dead Sea Scrolls, ancient texts of invaluable historical significance, were discovered in the mid-20th century in the caves of the Judean desert. The texts were mainly written on parchment and exhibit vast diversity in their states of preservation. One particular scroll, the 8-m-long Temple Scroll is especially notable because of its exceptional thinness and bright ivory color. The parchment has a layered structure, consisting of a collagenous base material and an atypical inorganic overlayer. We analyzed the chemistry of the inorganic layer using x-ray and Raman spectroscopies and discovered a variety of evaporitic sulfate salts. This points toward a unique ancient production technology in which the parchment was modified through the addition of the inorganic layer as a writing surface. Furthermore, understanding the properties of these minerals is particularly critical for the development of suitable conservation methods for the preservation of these invaluable historical documents.

Physical activity, body mass, and dietary habits are known to be important determinants of overall health status, but there is an evident lack of studies that examine these issues specifically in preschool children. The aim of this study was to identify associations that may exist between adhering to the Mediterranean diet (MD), levels of physical activity (PA), and body composition indices in apparently healthy preschool children from southern Croatia. Participants were 5- to 6-year-old preschoolers from the Mediterranean part of the country (the Split-Dalmatia County; n = 260, 126 females). Adherence to the MD was observed by the Mediterranean Diet Quality Index (KIDMED), PA level was evaluated by the Preschool-age Children’s Physical Activity Questionnaire (Pre-PAQ), and responses were collected from the parents. The participants’ waist circumferences (in cm), waist-to-hip ratios, and body mass index (in kg/m2, and in a z-score calculated relative to the normative value for age and sex) were used as indicators of body composition. All children were of the same age and tested over a one-month period of the same year as a part of the regular examination undertaken before attending elementary school. With only 6% of the children having a low KIDMED score, adherence to the MD was high. MD adherence was higher in girls (Chi-square = 15.31, p < 0.01) and children who live on the coast of the Adriatic Sea (Chi-square = 18.51, p < 0.01). A mixed effects logistic regression (with kindergarten as random factor) identified sedentary activity to be negatively associated with MD adherence (OR per point: 0.65, 95% CI: 0.44–0.91). High adherence to the MD in the studied sample may be attributed to regulated feeding in kindergarten. Considering that most Croatian elementary schools do not provide food to their students, MD adherence should be investigated later in life and also in other parts of the country where the MD is culturally less prevalent.

Condition monitoring enables companies from all industrial branches to maintain high availability of their automation systems. Thus, condition monitoring is a fundamental technology to maximize operational productivity by predicting potential fault occurrences that lead to unavailability of the automation systems. Today, performing condition monitoring is successfully achieved using custom-developed software solutions. However, the development of these software solutions is a challenging task as it requires extensive knowledge from multiple engineering disciplines. In this context, there is a high need for a systematic way to formalize this knowledge. Ontologies have had significant success in fulfilling this need by providing a formal model of domain concepts and their relations.In this paper, we present our condition monitoring ontology for automation systems and contribute with a conceptual model. This ontology incorporates ISO standards for condition monitoring and key performance indicators (KPIs). KPIs aggregate numerous sensor values in a few understandable numbers and, therefore, provide more efficient insight in condition of automation systems. To evaluate our work, we developed a condition monitoring knowledge-based system for a centrifugal separator based on the proposed ontology in an industrial project and report about lessons learned.

The process of beer brewing is very complex as it has to fulfill strict demands on the product quality as well as on the availability and the performance of the plant. As a consequence, a condition monitoring of the beer brewing process and its visualization plays an important role such that all relevant deviations are detected as early as possible by the production manager. While a general process for condition monitoring already exists, there currently exists no approach to realize this process for the domain of beer brewing.Therefore, this paper presents a condition monitoring system for the beer brewing process developed in an industrial project. This condition monitoring system is based on Key Performance Indicators (KPIs) that support the production manager in evaluating the actual state of the production processes. A set of brewery-specific KPIs are determined and discussed in the paper. In addition, software architecture and visualization of the KPIs in a brewery-specific dashboard are presented. We evaluate our concept at various beer breweries and report about lessons that we have learned.

The aim of this research is to create fully functional environment for real-life testing of various algorithms in mobile robotic. Main advantage, beside low-cost, is dealing with challenges of real-life implementation. Unlike simulation environments (e.g. MathLab), this setting will allow researchers to test their path planning, collision detection and other algorithms with real challenges, real robots and real static and dynamic obstacles. Environment is particularly adapted for swarm robotics and any other mobile robotics including 2D and 3D scenarios. Enabling researches to prove their algorithms in this environment allows significantly faster path to real implementation in industry, military and science. Concept is based on OpenCV library and low-cost hardware. Solution is based on visual positioning and motion vector detection. System allows controlling mobile robots via radio communication with the range up to 100m which allows practical application of the system. This research paper should be considered as a part of series of research papers published earlier.

Abstract The majority of past studies assessed the impact of oil price on stock returns using aggregate stock price index from different countries and assuming the effects to be symmetric. In this paper, we investigate asymmetric causality not only from oil price to stock returns but also from stock returns to oil price. To reduce aggregation bias, we use data from nine different sectors of the U.S. economy. We found that an increase in oil price causes returns of three sectors, while a decrease in oil price causes returns of four sectors, all in the short run. On the other hand, we found that an increase in returns in three sectors causes oil price to rise, while a decrease in returns in six sectors causes oil price to decline. We do not discover significant long-run causal relationship in either direction.

Abstract Poorly regulated and insufficiently supervised medical devices (MDs) carry high risk of performance accuracy and safety deviations effecting the clinical accuracy and efficiency of patient diagnosis and treatments. Even with the increase of technological sophistication of devices, incidents involving defibrillator malfunction are unfortunately not rare. To address this, we have developed an automated system based on machine learning algorithms that can predict performance of defibrillators and possible performance failures of the device which can affect performance. To develop an automated system, with high accuracy, overall dataset containing safety and performance measurements data was acquired from periodical safety and performance inspections of 1221 defibrillator. These inspections were carried out in period 2015–2017 in private and public healthcare institutions in Bosnia and Herzegovina by ISO 17,020 accredited laboratory. Out of overall number of samples, 974 of them were used during system development and 247 samples were used for subsequent validation of system performance. During system development, 5 different machine learning algorithms were used, and resulting systems were compared by obtained performance. The results of this study demonstrate that clinical engineering and health technology management benefit from application of machine learning in terms of cost optimization and medical device management. Automated systems, based on machine learning algorithms, can predict defibrillator performance with high accuracy. Systems based on Random Forest classifier with Genetic Algorithm feature selection yielded highest accuracy among other machine learning systems. Adoption of such systems will help in overcoming challenges of adapting maintenance and medical device supervision mechanism protocols to rapid technological development of these devices. Due to increased complexity of healthcare institution environment and increased technological complexity of medical devices, performing maintenance strategies in traditional manner is causing a lot of difficulties.

Objectives We sought to determine the feasibility and characterize the extinction kinetics of circulating cell-free tumor DNA (cfDNA) testing in endometrial and ovarian carcinomas (ECs, OCs) using a clinically-approved commercially-available assay. Methods Women with suspected EC/OC undergoing surgery were consented for tissue and plasma sampling including pre-operative and serial post-operative draws. Tumour tissue and patient-matched buffy coat was extracted for DNA and sequenced for somatic mutations using FINDIT™ panel assay. Plasma samples were extracted for cfDNA and sequenced using FOLLOWIT™, Illumina platform, and analyzed using Contextual Genomics’s QUALITY NEXUS analysis pipelines. Low-frequency variants were confirmed by digital droplet PCR. Results 44 individuals had sufficient tissue and follow-up for inclusion; 24 ECs (13 endometrioid, 10 high-grade serous (HGS), 1 clear cell(CC)), 18 OCs (17 HGS 1, CC), and 2 synchronous endometrial and ovarian carcinomas. Eight ECs and 15 OC cases were advanced stage (II-IV) with residual disease in 2 ECs and 5 OCs, 8 recurrence events and 3 deaths recorded. Compliance with plasma sampling was high(>95%) when requested in hospital or at routine surveillance visits but dropped to 68% for ‘extra’ study-associated visits. Analysis to date reveals cfDNA was detectable in pre-operative samples of 19 individuals (9 ECs, 10 OCs including 4 early stage) and 6/10 tested post-operatively. Normalization of conventional tumour markers post-operatively took a median of 3mo in contrast to rapid loss of detectable cfDNA. Conclusions cfDNA testing is feasible and may enhance surveillance of endometrial and ovarian carcinomas by reflecting i) volume of disease pre-/post-operatively, ii) response to therapy, and/or iii) recurrence.