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We investigate the applicability of the well-known multilevel Monte Carlo (MLMC) method to the class of density-driven flow problems, in particular the problem of salinisation of coastal aquifers. As a test case, we solve the uncertain Henry saltwater intrusion problem. Unknown porosity, permeability and recharge parameters are modelled by using random fields. The classical deterministic Henry problem is non-linear and time-dependent, and can easily take several hours of computing time. Uncertain settings require the solution of multiple realisations of the deterministic problem, and the total computational cost increases drastically. Instead of computing of hundreds random realisations, typically the mean value and the variance are computed. The standard methods such as the Monte Carlo or surrogate-based methods are a good choice, but they compute all stochastic realisations on the same, often, very fine mesh. They also do not balance the stochastic and discretisation errors. These facts motivated us to apply the MLMC method. We demonstrate that by solving the Henry problem on multi-level spatial and temporal meshes, the MLMC method reduces the overall computational and storage costs. To reduce the computing cost further, parallelization is performed in both physical and stochastic spaces. To solve each deterministic scenario, we run the parallel multigrid solver ug4 in a black-box fashion.
Highlights
• High resolution profile of C. pipiens' sugar diet has been obtained using UHPLC-MS.
• Artificial feeding using ornamental plants provides similar sugar profiles as observed in field collected mosquitoes.
• Metabolomic profiling found secondary metabolites and pollutants of anthropogenic use.
Abstract: Culex pipiens (Linnaeus, 1758) mosquitoes search plant sources of sugars to cope with the energetic demand of various physiological processes. The crop as part of the digestive system is devoted to the storage of sugar-based meal obtained from various nectars sources. The profiling of sugars and metabolites in the Culex pipiens’ crop is scarce, and only few studies used Liquid Chromatography – Mass Spectrometry (LC-MS), which provides broad detection for biomonitoring environmental substances and even contaminants in the sugar diet of mosquitoes populations.
Therefore, sugar and metabolite profiling were performed on crops obtained from mosquitoes exposed to plant nectar under laboratory or natural conditions by Ultra High-Performance LC-MS (UHPLC-MS). This method allowed us a precise quantitative and qualitative identification of sugar diet and associated environmental compounds in the crop of the mosquito C. pipiens. Under laboratory condition, mosquitoes were allowed to feed on either glucose solution, commercially-available flowers or field collected flowers. In addition, we collected mosquitoes from the field to compare those crop metabolomes with metabolome patterns occurring after nectar feeding in the lab.
The sugar quantities and quality obtained from the crops of mosquitoes collected in the field were similar to those crops obtained from mosquitoes that fed on commercially-available flowers and from field collected flowers with a limit of detection of 10 μg/L for sucrose, glucose and sucrose. Next to sugar compounds, we identified 2 types of amino acids, 12 natural products, and 9 pesticides.
Next to the diversity of sugar compounds, we could confirm that secondary metabolites and environmental pollutants are typically up taken from floral nectar sources by C. pipiens. The in-depth knowledge on mosquito–plant interactions may inspire the development and further optimization of mosquito trap systems and arboviral surveillance systems.
In a year marked by challenging market dynamics, the importance of ESG investments remains unwavering. But the wave of ESG regulations and requests generates a demand for more scalable ways to collect and analyze ESG data. The rise of AI could mark a turning point in an industry heavily burdened by reporting requirements, and unlock the true potential of ESG for businesses and investors alike.
Yes, they are. The securities services industry is at a tipping point of its digital transformation and will now see industry solutions to scale. We identify three fundamental drivers being adopted more broadly: cloud migration, data, and digitization. This triage also drives the scaling of Clearstream’s digital infrastructure D7, one of the leading digital infrastructures globally.
This study explores high-frequency cross-asset lead-lag relationships for various market microstructure dimensions. Utilizing data from stocks, futures, and exchange traded products, the findings uncover significant lead-lag patterns, particularly among fundamentally related instruments. Our results demonstrate that knowledge about lead-lag relationships can be leveraged for forecasting short-term changes in financial markets.
Customer loyalty is a critical measure for success, showing if a firm's product is received well by its customers. To understand its development over time, two fundamental questions must be answered: (I) How will current customers' loyalty develop, and (II) will new customers' loyalty differ from current customers' loyalty? The authors empirically answer these questions based on a data set including ~500 B2B web technologies with jointly ~325 million customers spanning over 24 years. They show that loyalty hardly develops and, if so, it rather decreases than increases. The loyalty of current customers rarely changes and, if so, rather increases than decreases. New customers are most likely less loyal than current customers. These results show that by failing to account for these underlying developments, stakeholders, in most cases, draw the wrong conclusions about product value measured via customer lifetime value.
Existing table retrieval approaches estimate each table’s relevance for a particular information need and return a ranking of the most relevant tables. This approach is not ideal since the returned tables often include irrelevant data and the required information may be scattered across multiple tables. To address these issues, we propose the idea of fine-grained structured table retrieval and present our vision of R2D2, a system which slices tables into small tiles that are later composed into a structured result that is tailored to the user-provided information need. An initial evaluation of our approach demonstrates how our idea can improve table retrieval and relevant downstream tasks such as table question answering.
Generative AI is a game changer – also in the financial sector. Institutions and their IT service providers need to consider carefully: Which AI approach will enable them to implement optimal solutions for themselves and their customers in this highly regulated environment? How did Finanz Informatik, as the savings banks’ digitalization partner, proceed here?
The genus Tekellina Levi, 1957 is currently composed of ten species, six of which are Neotropical. They are small-sized spiders (0.9 to 1.5 mm), with a wide distribution, with a great diversity in the Neotropical Region and well represented in Brazil. In this article, males and females of the species Tekellina bella Marques & Buckup, 1993 and T. crica Marques & Buckup, 1993 are redescribed and illustrated. The female of Tekellina minor Marques & Buckup, 1993 is described and illustrated for the first time. New records are included for Neotropical species. Tekellina guaiba Marques & Buckup, 1993 is synonymized with T. pretiosa Marques & Buckup, 1993. Three new species are described for Brazil: Tekellina picurrucha Rodrigues & Estol sp. nov. (São Paulo, Paraná and Rio Grande do Sul), Tekellina miuda Rodrigues & Estol sp. nov. (São Paulo and Paraná) and Tekellina miudinha Rodrigues & Estol sp. nov. (São Paulo). Distribution maps with new records and an identification key of the Neotropical species are also presented.
The family Dendrodorididae has a global distribution, with prevalence in tropical and subtropical intertidal zones. Three species of Dendrodorididae were collected from the intertidal zone of the northern coast of the Persian Gulf in Iran. Based on anatomical, histological, and molecular investigations they can be assigned to Dendrodoris fumata, Dendrodoris nigra, and a new species of Doriopsilla, D. aroni sp. nov. Molecular analyses of CO1 and 16S, including all genera of Dendrodorididae, members of the sister taxon Phyllidiidae, and other dorid outgroups resulted in a polyphyletic genus Dendrodoris, which is in contrast to the nuclear gene studies. Our molecular results confirm the differentiation between Dendrodoris rubra and D. fumata. Dendrodoris nigra, D. fumata, and D. krusensternii each consist of several clades, indicating cryptic species complexes requiring further investigation. We describe the presence of bacteria for the first time in the vestibular gland of D. fumata. Validation of the specimens of Doriopsilla from the Persian Gulf as a new species is supported by haplotype networking, genetic distance, and ABGD analyses of mitochondrial genes. Our CO1 analysis confirms a previous hypothesis that Cariopsilla is a junior synonym of Doriopsilla.