Methodologies for the analysis of pesticides and pharmaceuticals in sediments and plant tissue

Eco-technologies that utilize natural processes involving wetland vegetation, soil and their associated microbial assemblages are increasingly used for the removal of contaminants of emerging concern (CECs) from polluted water. However, information on removal processes in these systems is not always available, possibly due to the lack of simple and robust methodologies for analysis of CECs in complex matrices such as sediment and plant tissue. The aim of the present study was to use a simple and fast procedure based on ultrasonic extraction (USE) and reduced clean-up procedures to analyse 8 pesticides and 9 pharmaceuticals by high-performance liquid chromatography (HPLC) coupled with diode array detector. The established methods demonstrated suitable sensitivity and reliability, and proved fit-for-purpose in quantifying multiple classes of pesticides and pharmaceuticals. For sediments, extraction with methanol/acetone (95:5, v/v) followed by a simple evaporation to dryness and redissolution (water:methanol 50:50) provided acceptable recovery (50 - 101%) and RSD 64%) with RSD < 22% determined using different types of wetland plants. The methodology has been successfully applied in different studies on the fate of emerging contaminants in water treatment eco-technology systems

The Third International Verification of Neural Networks Competition (VNN-COMP 2022): Summary and Results

This report summarizes the 3rd International Verification of Neural Networks Competition (VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), which was collocated with the 34th International Conference on Computer-Aided Verification (CAV). VNN-COMP is held annually to facilitate the fair and objective comparison of state-of-the-art neural network verification tools, encourage the standardization of tool interfaces, and bring together the neural network verification community. To this end, standardized formats for networks (ONNX) and specification (VNN-LIB) were defined, tools were evaluated on equal-cost hardware (using an automatic evaluation pipeline based on AWS instances), and tool parameters were chosen by the participants before the final test sets were made public. In the 2022 iteration, 11 teams participated on a diverse set of 12 scored benchmarks. This report summarizes the rules, benchmarks, participating tools, results, and lessons learned from this iteration of this competition.Comment: Corrected a small error in instance-wise results; 54 pages, 27 tables, and 16 figure

Unsupervised denoising for sparse multi-spectral computed tomography

Multi-energy computed tomography (CT) with photon counting detectors (PCDs) enables spectral imaging as PCDs can assign the incoming photons to specific energy channels. However, PCDs with many spectral channels drastically increase the computational complexity of the CT reconstruction, and bespoke reconstruction algorithms need fine-tuning to varying noise statistics. \rev{Especially if many projections are taken, a large amount of data has to be collected and stored. Sparse view CT is one solution for data reduction. However, these issues are especially exacerbated when sparse imaging scenarios are encountered due to a significant reduction in photon counts.} In this work, we investigate the suitability of learning-based improvements to the challenging task of obtaining high-quality reconstructions from sparse measurements for a 64-channel PCD-CT. In particular, to overcome missing reference data for the training procedure, we propose an unsupervised denoising and artefact removal approach by exploiting different filter functions in the reconstruction and an explicit coupling of spectral channels with the nuclear norm. Performance is assessed on both simulated synthetic data and the openly available experimental Multi-Spectral Imaging via Computed Tomography (MUSIC) dataset. We compared the quality of our unsupervised method to iterative total nuclear variation regularized reconstructions and a supervised denoiser trained with reference data. We show that improved reconstruction quality can be achieved with flexibility on noise statistics and effective suppression of streaking artefacts when using unsupervised denoising with spectral coupling

Temporal Variability of Organic C and Nitrate in a Shallow Aquifer

The loading of organic substrates into shallow aquifers may follow seasonal cycles, which will impact the transport and fate of agrichemicals. The objective of this research was to measure temporal changes in the groundwater dissolved organic C (DOC) and nitrate concentrations. Groundwater monitoring wells were installed and sediment samples from the aquifer were collected in 1991. Sediment samples were used to evaluate denitrification potentials, while water samples were collected at periodic intervals in 1992 and 1993 from the surface of the aquifer. Water samples were analyzed for nitrate-N and DOC-C. Denitrification was observed in sediment amended with nitrate and incubated under anaerobic conditions at 10°C. Addition of algae lazed biomass increased denitrification, establishing that denitrification was substrate limited. In the aquifer, DOC concentrations followed seasonal patterns. DOC concentrations were highest following spring recharge and then decreased. Peak timing indicates that freezing and thawing were responsible for seasonal DOC patterns. These findings show that seasonally driven physical processes, such as freezing and thawing, influence organic substrate transport from surface to subsurface environments, and that this process should be taken into account when assessing agrichemical detoxification rates in shallow aquifers

Local edge computing for radiological image reconstruction and computer-assisted detection: A feasibility study

Computational requirements for data processing at different stages of the radiology value chain are increasing. Cone beam computed tomography (CBCT) is a diagnostic imaging technique used in dental and extremity imaging, involving a highly demanding image reconstruction task. In turn, artificial intelligence (AI) assisted diagnostics are becoming increasingly popular, thus increasing the use of computation resources. Furthermore, the need for fully independent imaging units outside radiology departments and with remotely performed diagnostics emphasize the need for wireless connectivity between the imaging unit and hospital infrastructure. In this feasibility study, we propose an approach based on a distributed edge-cloud computing platform, consisting of small-scale local edge nodes, edge servers with traditional cloud resources to perform data processing tasks in radiology. We are interested in the use of local computing resources with Graphics Processing Units (GPUs), in our case Jetson Xavier NX, for hosting the algorithms for two use-cases, namely image reconstruction in cone beam computed tomography and AI-assisted cancer detection from mammographic images. Particularly, we wanted to determine the technical requirements for local edge computing platform for these two tasks and whether CBCT image reconstruction and breast cancer detection tasks are possible in a diagnostically acceptable time frame. We validated the use-cases and the proposed edge computing platform in two stages. First, the algorithms were validated use-case-wise by comparing the computing performance of the edge nodes against a reference setup (regular workstation). Second, we performed qualitative evaluation on the edge computing platform by running the algorithms as nanoservices. Our results, obtained through real-life prototyping, indicate that it is possible and technically feasible to run both reconstruction and AI-assisted image analysis functions in a diagnostically acceptable computing time. Furthermore, based on the qualitative evaluation, we confirmed that the local edge computing capacity can be scaled up and down during runtime by adding or removing edge devices without the need for manual reconfigurations. We also found all previously implemented software components to be transferable as such. Overall, the results are promising and help in developing future applications, e.g., in mobile imaging scenarios, where such a platform is beneficial