Modelling short- and long-term dependencies of clustered high-threshold exceedances in significant wave heights

The peaks-over-threshold (POT) method has a long tradition in modelling extremes in environmental variables. However, it has originally been introduced under the assumption of independently and identically distributed (iid) data. Since environmental data often exhibits a time series structure, this assumption is likely to be violated due to short-and long-term dependencies in practical settings, leading to clustering of high-threshold exceedances. In this paper, we first review popular approaches that either focus on modelling short-or long-range dynamics explicitly. In particular, we consider conditional POT variants and the Mittag–Leffler distribution modelling waiting times between exceedances. Further, we propose a new two-step approach capturing both short-and long-range correlations simultaneously. We suggest the autoregressive fractionally integrated moving average peaks-over-threshold (ARFIMA-POT) approach, which in a first step fits an ARFIMA model to the original series and then in a second step utilises a classical POT model for the residuals. Applying these models to an oceanographic time series of significant wave heights measured on the Sefton coast (UK), we find that neither solely modelling short-nor long-range dependencies satisfactorily explains the clustering of extremes. The ARFIMA-POT approach, however, provides a significant improvement in terms of model fit, underlining the need for models that jointly incorporate short-and long-range dependence to address extremal clustering, and their theoretical justification. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights

The peaks-over-threshold (POT) method has a long tradition in modelling extremes in environmental variables. However, it has originally been introduced under the assumption of independently and identically distributed (iid) data. Since environmental data often exhibits a time series structure, this assumption is likely to be violated due to short- and long-term dependencies in practical settings, leading to clustering of high-threshold exceedances. In this paper, we first review popular approaches that either focus on modelling short- or long-range dynamics explicitly. In particular, we consider conditional POT variants and the Mittag–Leffler distribution modelling waiting times between exceedances. Further, we propose a new two-step approach capturing both short- and long-range correlations simultaneously. We suggest the autoregressive fractionally integrated moving average peaks-over-threshold (ARFIMA-POT) approach, which in a first step fits an ARFIMA model to the original series and then in a second step utilises a classical POT model for the residuals. Applying these models to an oceanographic time series of significant wave heights measured on the Sefton coast (UK), we find that neither solely modelling short- nor long-range dependencies satisfactorily explains the clustering of extremes. The ARFIMA-POT approach, however, provides a significant improvement in terms of model fit, underlining the need for models that jointly incorporate short- and long-range dependence to address extremal clustering, and their theoretical justification

Interactions in IS Project Portfolio Selection - Status Quo and Perspectives

One central and important requirement for IS project portfolio selection is the adequate consideration of project interactions. However, the IS discipline notably lacks a common understanding of the nature of project interactions and their impact on IS project portfolio selection. To remedy this we conduct a systematic and interdisciplinary literature review thereby providing a starting point for a cumulative research tradition. The main contribution of this paper is the development of a taxonomy to summarize the current state-of-the-art. Thereby, we provide a basis enabling researchers to develop integrated approaches. Based on the identified research gaps we formulate a research agenda for the field of IS project portfolio selection considering interactions

In the back of your mind: Cortical mapping of paraspinal afferent inputs

Topographic organisation is a hallmark of vertebrate cortex architecture, characterised by ordered projections of the body's sensory surfaces onto brain systems. High-resolution functional magnetic resonance imaging (fMRI) has proven itself as a valuable tool to investigate the cortical landscape and its (mal-)adaptive plasticity with respect to various body part representations, in particular extremities such as the hand and fingers. Less is known, however, about the cortical representation of the human back. We therefore validated a novel, MRI-compatible method of mapping cortical representations of sensory afferents of the back, using vibrotactile stimulation at varying frequencies and paraspinal locations, in conjunction with fMRI. We expected high-frequency stimulation to be associated with differential neuronal activity in the primary somatosensory cortex (S1) compared with low-frequency stimulation and that somatosensory representations would differ across the thoracolumbar axis. We found significant differences between neural representations of high-frequency and low-frequency stimulation and between representations of thoracic and lumbar paraspinal locations, in several bilateral S1 sub-regions, and in regions of the primary motor cortex (M1). High-frequency stimulation preferentially activated Brodmann Area (BA) regions BA3a and BA4p, whereas low-frequency stimulation was more encoded in BA3b and BA4a. Moreover, we found clear topographic differences in S1 for representations of the upper and lower back during high-frequency stimulation. We present the first neurobiological validation of a method for establishing detailed cortical maps of the human back, which might serve as a novel tool to evaluate the pathological significance of neuroplastic changes in clinical conditions such as chronic low back pain

Towards Personalized Explanations for AI Systems: Designing a Role Model for Explainable AI in Auditing

Due to a continuously growing repertoire of available methods and applications, Artificial Intelligence (AI) is becoming an innovation driver for most industries. In the auditing domain, initial approaches of AI have already been discussed in scientific discourse, but practical application is still lagging behind. Caused by a highly regulated environment, the explainability of AI is of particular relevance. Using semi-structured expert interviews, we identified stakeholder specific requirements regarding explainable AI (XAI) in auditing. To address the needs of all involved stakeholders a theoretical role model for AI systems has been designed based on a systematic literature review. The role model has been instantiated and evaluated in the domain of financial statement auditing using focus groups of domain experts. The resulting model offers a foundation for the development of AI systems with personalized explanations and an optimized usage of existing XAI methods

Regional review: the hydrology of the Okavango Delta, Botswana—processes, data and modelling

The wetlands of the Okavango Delta accommodate a multitude of ecosystems with a large diversity in fauna and flora. They not only provide the traditional livelihood of the local communities but are also the basis of a tourism industry that generates substantial revenue for the whole of Botswana. For the global community, the wetlands retain a tremendous pool of biodiversity. As the upstream states Angola and Namibia are developing, however, changes in the use of the water of the Okavango River and in the ecological status of the wetlands are to be expected. To predict these impacts, the hydrology of the Delta has to be understood. This article reviews scientific work done for that purpose, focussing on the hydrological modelling of surface water and groundwater. Research providing input data to hydrological models is also presented. It relies heavily on all types of remote sensing. The history of hydrologic models of the Delta is retraced from the early box models to state-of-the-art distributed hydrological models. The knowledge gained from hydrological models and its relevance for the management of the Delta are discusse

How to develop health-promoting food supplements by using single-use bioreactors

There is currently considerable interest in alternative and sustainable production methods for healthy foods. The cultivation of plant cell cultures in suitable bioreactors instead of growing whole plants on the field may be a solution. In this way, the cell cultures of interesting plant species can be established independent of the location. Furthermore, secondary metabolism can be specifically controlled during mass propagation of the cells. In other words, the expression of compounds promoting health and wellbeing can be supported, and the formation of substances with adverse health effects can be suppressed. We used this approach to make cacao powder and to produce a ‘cell culture chocolate’ by growing suspension cells from Theobroma cacao in a Flexsafe RM 20L bag with a screw cap from a BIOSTAT RM 20/50. The cell line (dark culture) was established from a well-growing and friable callus clone, and has a doubling time of 4 days. It provided up to 40% higher concentrations of the polyphenols epicatechine, procyanidine B1, B2 and C1, and cinnamtannine A2 than cocoa beans from pods grown in Puerto Rico. The alkaloids caffeine and theobromine were absent in the cell culture grown in MS-medium. On day 16, about 300 g biomass (fresh weight) was harvested from the wave-mixed single-use bioreactor operated in feeding mode. Addition of an antifoam agent and pH-regulator was not required. The biomass was freeze-dried, resulting in in vitro cacao powder that was roasted and milled before adding sugar, lecithin and cocoa butter. 3 blocks of dark chocolate (70%) were produced, which provided the experts on the ZHAW`s sensory panel with a unique taste experience. The flavour was intensive and complex, citric and berry flavours being predominant. The results demonstrate the suitability of wave-mixed bioreactors for the development of plant cell-based health-promoting food and food ingredients. Subsequent studies will focus on the influence of power input and shear stress on polyphenol formation, and the development of a scalable low-cost bioreactor

Molecular data storage with zero synthetic effort and simple read-out

Compound mixtures represent an alternative, additional approach to DNA and synthetic sequence-defined macromolecules in the field of non-conventional molecular data storage, which may be useful depending on the target application. Here, we report a fast and efficient method for information storage in molecular mixtures by the direct use of commercially available chemicals and thus, zero synthetic steps need to be performed. As a proof of principle, a binary coding language is used for encoding words in ASCII or black and white pixels of a bitmap. This way, we stored a 25 × 25-pixel QR code (625 bits) and a picture of the same size. Decoding of the written information is achieved via spectroscopic (1H NMR) or chromatographic (gas chromatography) analysis. In addition, for a faster and automated read-out of the data, we developed a decoding software, which also orders the data sets according to an internal “ordering” standard. Molecular keys or anticounterfeiting are possible areas of application for information-containing compound mixtures