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.

The purpose of a car jack is lifting the car and maintaining it at a certain height during different repairs. This paper focuses on the design of car jack, which belongs to the basic equipment of cars. Cars jacks are used mainly for changing tires and small repairs of a car. The aim of this paper was to create a parametric CAD model of a car jack and carry out numerical structural analysis of the car jack using the created parametric CAD model. The development of the parametric CAD model and structural analysis was performed using the CATIA V5 system. This paper describes the modern way of creating more complex mechanisms, which support quick modification of its parameters, and thus the entire design. The whole model of the car jack was parametrized. The stresses obtained by finite element method (FEM) analysis were confirmed with the analytical calculation in characteristic parts of the design, with some exceptions. At the end of the paper, an analysis of the obtained results was performed, on the basis of which specified conclusions were made.

Changing the optical fiber parameters during long-term use can not be mathematically calculated. This paper analyses the change of optical fibers from the aspect of ageing under the influence of transmitted signals and the aspect of water influences. The analysis was carried out in such a way as to compare the measurements carried out after the installation of optical cables and measurements after 16 years of use. The analysis has been shown to vary greatly the loss of fiber optic splice for different wavelengths. These different loss for wavelengths 1310 nm and 1550 nm suggest that the loss of optical fiber has increased due to the impact of water.

This paper addresses the problem of 3D registration of outdoor environments combining heterogeneous datasets acquired from unmanned aerial (UAV) and ground (UGV) vehicles. In order to solve this problem, we introduced a novel Scale Invariant Registration Method (SIRM) for semi-automated registration of 3D point clouds. The method is capable of coping with an arbitrary scale difference between the point clouds, without any information about their initial position and orientation. Furthermore, the SIRM does not require having a good initial overlap between two heterogeneous datasets. Our method strikes an elegant balance between the existing fully automated 3D registration systems (which often fail in the case of heterogeneous datasets and harsh outdoor environments) and fully manual registration approaches (which are labour-intensive). The experimental validation of the proposed 3D heterogeneous registration system was performed on large-scale datasets representing unstructured and harsh outdoor environments, demonstrating the potential and benefits of the proposed 3D registration system in real-world environments.

IoT devices have a wide spectrum of applications in the real-life environment. While these applications range based on the area covered, having the best scenario related to the devices covering the optimal area is a challenge. In this work, we consider the improvement of the industrial laboratory by transferring it to the smart lab using the IoT devices. We analyzed the tradeoffs between different scenarios of the smart lab with the focus on the security and congestion of the network and its effects on the overall performance. For the smart lab case study we can conclude that security-enabled feature will not significantly affect the performance of the smart lab compared to the benefits of the IoT-integrated devices on the overall improvement of the lab experience given that the traditional lab had significant time delay.

In process automation installations, the I/O system connect the field devices to the process controller over a fieldbus, a reliable, real-time capable communication link with signal values cyclical being exchanged with a 10–100 millisecond rate. If a deviation from intended behaviour occurs, analyzing the potentially vast data recordings from the field can be a time consuming and cumbersome task for an engineer. For the engineer to be able to get a full understanding of the problem, knowledge of the used I/O configuration is required. In the problem report, the configuration description is sometimes missing. In such cases it is difficult to use the recorded data for analysis of the problem.In this paper we present our ongoing work towards using neural network models as assistance in the interpretation of an industrial fieldbus communication recording. To show the potential of such an approach we present an example using an industrial setup where fieldbus data is collected and classified. In this context we present an evaluation of the suitability of different neural net configurations and sizes for the problem at hand.

Combating eutrophication requires changes in land and water management in agricultural catchments and implementation of mitigation measures to reduce phosphorus (P), nitrogen (N) and suspended sediment (SS) losses. To date, such mitigation measures have been built in many agricultural catchments, but there is a lack of studies evaluating their effectiveness. Here we evaluated the effectiveness of mitigation measures in a clay soil-dominated headwater catchment by combining the evaluation of long-term and high-frequency data with punctual measurements upstream and downstream of three mitigation measures: lime-filter drains, a two-stage ditch, and a sedimentation pond. Long-term hydrochemical data at the catchment outlet showed a significant decrease in P (-15%) and SS (-28%) and an increase in nitrate nitrogen (NO-N, +13%) concentrations. High-frequency (hourly) measurements with a wet-chemistry analyzer (total and reactive P) and optical sensor (NO-N and SS) showed that the catchment is an abundant source of nutrients and sediments and that their transport is exacerbated by prolonged drought and resuspension of stream sediments during storm events. Lime-filter drains showed a decrease in SS by 76% and total P by 80% and an increase in NO-N by 45% compared with traditional drains, potentially indicating pollution swapping. The effectiveness of two-stage ditch and sedimentation pond was less evident and depended on the prevalent hydrometeorological conditions that drove the resuspension of bed sediments and associated sediment-bound P transport. These results suggest that increased frequency of prolonged drought due to changing weather patterns and resuspension of SS and sediment-bound P during storm events can override the generally positive effect of mitigation measures.

The evolution of phosphorus (P) management decision support tools (DSTs) and systems (DSS), in support of food and environmental security has been most strongly affected in developed regions by national strategies (i) to optimize levels of plant available P in agricultural soils, and (ii) to mitigate P runoff to water bodies. In the United States, Western Europe, and New Zealand, combinations of regulatory and voluntary strategies, sometimes backed by economic incentives, have often been driven by reactive legislation to protect water bodies. Farmer-specific DSSs, either based on modeling of P transfer source and transport mechanisms, or when coupled with farm-specific information or local knowledge, have typically guided best practices, education, and implementation, yet applying DSSs in data poor catchments and/or where user adoption is poor hampers the effectiveness of these systems. Recent developments focused on integrated digital mapping of hydrologically sensitive areas and critical source areas, sometimes using real-time data and weather forecasting, have rapidly advanced runoff modeling and education. Advances in technology related to monitoring, imaging, sensors, remote sensing, and analytical instrumentation will facilitate the development of DSSs that can predict heterogeneity over wider geographical areas. However, significant challenges remain in developing DSSs that incorporate "big data" in a format that is acceptable to users, and that adequately accounts for catchment variability, farming systems, and farmer behavior. Future efforts will undoubtedly focus on improving efficiency and conserving phosphate rock reserves in the face of future scarcity or prohibitive cost. Most importantly, the principles reviewed here are critical for sustainable agriculture.

To achieve efficient and flexible production at affordable prices, industrial automation is pushed towards a digital transformation. Such a transformation assumes an enhancement of current Industrial Automated Control Systems with a large amount of IoT-devices, forming an Industrial Internet of Things (IIoT). The aim is to enable a shift from automatic towards autonomous control in such systems. This paper discusses some of the main challenges IIoT systems are facing with respect to cybersecurity. We discuss our findings in an example of a flow-control loop, where we apply a simple threat model based on the STRIDE method to deduce cybersecurity requirements in an IIoT context. Moreover, the identified requirements are assessed in the light of current state of the art solutions, and a number of challenges are discussed with respect to a large-scale IIoT system, together with some suggestions for future work.

There is a growing interest in using the Blockchain for resolving IoT security and trustworthiness issues existing in today’s complex systems. Blockchain concerns trust in peer to peer networks by providing a distributed tamper-resistant ledger. However, the combination of these two emerging technologies might create new problems and vulnerabilities that attackers might abuse.In this paper, we aim to investigate the trust mechanism of Lightweight Scalable BlockChain (LSB), that is a Blockchain specifically designed for Internet of Things networks, to show that a malicious participant in a Blockchain architecture have possibility to pursue an On-Off attack and downgrade the integrity of the distributed ledger. We choose a remote software update process as an instance to represent this violation. Finally, using the actor-based language Rebeca, we provide a model of a system under attack and verify the described attack scenario.

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.

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 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.