Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty modeling in wind power generation prediction by neural networks and bootstrapping

Accurate short-term wind power forecasting with quantification of the associated uncertainty is crucial for the management of energy systems including wind power generation. On top of the inherent uncertainty in wind speed, it is necessary to account also for the uncertainty in the relationship between wind speed and the corresponding power production, typically described by a power curve whose characteristic parameters are not precisely known in practice. In this paper, we propose a novel approach to wind power forecasting with uncertainty quantification. The approach can be schematized in two steps: first, short-term estimation of wind speed prediction intervals (PIs) is performed within a multi-objective optimization framework worked out by non-dominated sorting genetic algorithm–II (NSGA-II); then, the uncertainty in wind speed and the uncertainty in the power curve are combined via a bootstrap sampling technique, thus obtaining wind power PIs with same coverage as the wind speed PIs. ha l-0 08 38 77 2, v er si on 1 26 J un 2 01 3 Author manuscript, published in "Esrel 2013, Netherlands (2013)"
Comparison of experimental identification techniques for a nonlinear SDOF system

Several experimental nonlinear identification techniques have been presented in the literature. In this paper, several of these techniques are applied to a single degree of freedom system and the results are compared. We apply a time-domain technique and two frequency-domain techniques to the second mode of a cantilevered beam. The frequency-domain techniques include a backbone curve-fitting technique based on the describing function method and an amplitude and frequency sweep technique based on the method of multiple-scales. The equation of motion is derived from a Lagrangian using the assumed-modes method. Based on the form of the equation of motion, we note that the restoring-force-surface method cannot be used to determine the equation of motion. Differences in the parameter estimates are discussed. We conclude by discussing the limitations we encountered for each technique. These include the inability to separate nonlinear curvature and inertia effects and problems with estimating the coefficients of small terms with the time-domain technique.
Local Perceptions of Social-Ecological Change on the McKenzie: Implications for Resilience

Local Perceptions of Social-Ecological Change on the McKenzie: Implications for Resilience This study sought to catalog local knowledge of long-term residents of the McKenzie River Valley as it pertains to landscape and community change and provide a general assessment of factors affecting the local social-ecological system’s resilience. Residents interviewed indicated that dramatic changes driven by market competition, timber industry changes, increased regulation, and rural restructuring have occurred in both the landscape and community. The changes that have transpired as a result have redefined the relationship between the community and the landscape, moving away from local dependence on timber harvests to a relationship oriented around tourism and other ecosystem services. In doing so the community has transitioned from one with a logging community identity to one that has begrudgingly transitioned to a retirement and vacation community. Resilience thinking, a framework for assessing a social-ecological system’s ability to adapt and confront forces of change, indicates that the social-ecological system in the MRV is still in the midst of redefining the systems thresholds and key feedbacks. As a result of low institutional capacity the system is vulnerable to continued drivers of change from outside the local system. Using a modified version of Ostrom’s (2009) framework for the analysis of social-ecological systems, this study recommends policymakers and policy entrepreneurs take three key steps to facilitate enhanced system resilience: 1) ensure transboundary management strategies are put in place that transcend landownership classifications; 2) tighten system feedbacks to include more local influence; 3) develop local multilayered institutions organized vertically and horizontally.
Electric field characterization of atmospheric pressure Helium plasma jets through numerical simulations and comparisons with experiments

In this PhD thesis numerical simulations based on a 2D fluid model are used to characterize pulsed Helium plasma jets. The model for He plasma jets is developed to describe He jets flowing in N2 and O2 atmospheres and interacting with targets. The discharge dynamics in He jets impacting a grounded metallic target is analyzed with both positive and negative polarities of applied voltage. The temporal and spatial evolutions of electric field associated to the first and rebound ionization fronts are in good qualitative agreement with recent electric field measurements. Then, the plasma-target interaction occurring between a positive discharge and a BSO dielectric target is investigated in detail and results are directly compared with experiments. A good agreement is obtained between simulations and experiments concerning the temporal and spatial profiles of electric field. Maximum values of electric field inside the target of 5 kV.cm−1 are found. The high electric field in the plasma of the order of dozens of kV.cm−1 is severely depleted by the change of permittivity. As a result, the electric field experienced inside the target is almost exclusively originated by the high values of surface charge deposited on the target surface. Finally, the influence of the electric field evolutions on the production of chemically active species close to the targets is evaluated. It is shown that with metallic targets the synergy between the first and rebound fronts increases species production close to the target.
Sukzessionslinien in basenreicher offener Sandvegetation des Binnenlandes - Ergebnisse aus Untersuchungen von Dauerbeobachtungsflächen

Successional pathways in base-rich open inland sand vegetation: results from permanent-plot studies The typical successional pathway in base-rich inland sand ecosystems leads from pioneer stages of the class Koelerio-Corynephoretea to species-rich stands of Allio-Stipetum (Festuco-Brometea). A further successional pathway is characterised by the development of ruderalized species-poor stands, which are dominated by generalistic competitive graminoids. We investigated altogether 32 non-managed permanent plots for up to 15 years. This enables us to differentiate successional lines and duration as well as changes in species composition in the course of succession. The database consists of relevés and derived information (e. g. Ellenberg indicator values); for selected plots supplementary data regarding soil parameters (N, P, pH) were available. An analysis of all plot data reveals relative stability of open sand vegetation on nutrient-poor stands for the investigated period. Pioneer communities of Sileno conicae-Cerastietum semidecandri s. l. and Jurineo-Koelerietum glaucae in part show structural changes (e.g. increase of the cryptogam layer). Stipa capillata increased in some stands of the Jurineo-Koelerietum in 11–12 years, which corresponds to a decrease of Koeleria glauca and partly to a substitution of Tortula ruraliformis by Hypnum cupressi289 Suess_Tuexenia 30 28.05. 10 07:55 Seite 289
Ontology-Based Data Federation - A Framework Proposal

Ontology-based data access (OBDA) is a well established approach to information management that facilitates the access to relational data sources through the mediation of a conceptual domain view, given in terms of an ontology, and the use of a declarative mapping between the data layer and the ontology. We formally introduce here the notion of ontology-based data federation (OBDF) to denote a framework that combines OBDA with a data federation layer where multiple heterogeneous sources are virtually exposed as a single relational database. We discuss opportunities and challenges of OBDF, and propose novel techniques to make query answering in the OBDF setting more efficient. Our techniques are validated through an extensive experimental evaluation based on the Berlin SPARQL Benchmark. This work is an abridged version of [1].
On the Throughput of Wireless Powered Communication Systems With a Multiple Antenna Bidirectional Relay

We consider an amplify-and-forward-based relay network comprising a hybrid access point, multi-antenna bidirectional relay and user terminal (UT). The relay first harvests energy using a power splitting (PS) scheme and then transfers power wirelessly to the UT. We analyze the throughput assuming maximum ratio combining in conjunction with a linear combination of information beamforming and energy beamforming at the relay. In particular, we derive two new closed-form approximations for the ergodic throughput in the asymptotic regime for a large number of antennas at the relay. We also present new solutions for the optimal beamforming vector and PS factor that maximize throughput. Finally, all the analytical results are corroborated through simulations.
ALS-associated FUS mutation reshapes the RNA and protein composition and dynamic of Stress Granules.

Stress Granules (SG) formation is a cellular protection mechanism in harmful conditions, constituting a storage for untranslated mRNAs and RNA-binding proteins (RBPs); however, these condensates can turn into pathological aggregates, that in neurons are related to the onset of neurodegenerative diseases, like Amyotrophic Lateral Sclerosis (ALS). Mutations in the RBP FUS leading to its cytoplasmic mis-localization are causatively linked to ALS, since they mediate its accumulation in SGs, triggering their transition towards cytotoxic inclusions. Here, we describe the SG transcriptome in a neural context and compare with datasets from other systems, identifying both common rules and diversifying characteristics for SG recruitment of transcripts. We demonstrate that SG dynamics and RNA content are strongly modified by the incorporation of aberrantly localized mutant FUS, switching to a more unstructured, AU-rich SG transcriptome and losing a wide population of GC-rich, structured RNAs. We show that these alterations mainly depend on mutant FUS which favors the SG incorporation of its protein interactors and in turn of their target RNAs. Our data give a comprehensive view of the molecular differences between physiological and pathological SG in ALS conditions, showing how a single mutation is sufficient to impact the RNA and protein population of these condensates.
Influenza A virus activates the unfolded protein response and induces the accumulation of insoluble protein aggregates that are essential for efficient viral propagation

Influenza A virus (IAV) is one of the main causes of annual respiratory epidemics in humans. IAV employs multiple strategies to evade host immunity and hijack cellular mechanisms to support proper virion formation and propagation. Some of these strategies encompass the manipulation of pathways involved in protein homeostasis, leading to changes in the host proteome and protein distribution within the cell. In this study, we performed a detailed analysis of the interplay between IAV and the host cells’ proteostasis mechanisms throughout the entire infectious cycle. We reveal that IAV infection induces the activation of the inositol requiring enzyme 1 (IRE1) branch of the unfolded protein response (UPR), at an infection stage that coincides with high rates of viral protein translation. This activation is particularly important for infection, as attenuation of virus production was observed upon IRE1 inhibition. Concomitantly to UPR activation, we observed the accumulation of virus-induced insoluble protein aggregates, which contain both viral and host proteins and are associated with a dysregulation of the host cell RNA metabolism. We demonstrate that this accumulation is important for IAV propagation, as its prevention using a quinoline-steroid hybrid compound significantly reduces the number of produced infectious virus particles. Our data suggests that the formation of these insoluble protein aggregates favors the final steps of the infection cycle, more specifically the virion assembly. Our findings reveal additional mechanisms by which IAV disrupts the host cell proteostasis to favor infection and uncover new cellular targets that can be explored for the development of host-directed antiviral strategies.
miRNA and mRNA Signatures in Human Acute Kidney Injury Tissue

Acute kidney injury (AKI) is an important contributor to the development of chronic kidney disease (CKD). There is a need to understand molecular mediators that drive either recovery or progression to CKD. In particular, the role of miRNA and its regulatory role in AKI is poorly understood. We performed miRNA and mRNA sequencing on biobanked human kidney tissues obtained in the routine clinical care of patients with the diagnoses of AKI and minimal change disease (MCD), in addition to nephrectomized (Ref) tissue from individuals without known kidney disease. Transcriptomic analysis of mRNA revealed that Ref tissues exhibited a similar injury signature to AKI, not identified in MCD samples. The transcriptomic signature of human AKI was enriched with genes in pathways involved in cell adhesion and epithelial-to-mesenchymal transition (e.g., CDH6, ITGB6, CDKN1A). miRNA DE analysis revealed upregulation of miRNA associated with immune cell recruitment and inflammation (e.g., miR-146a, miR-155, miR-142, miR-122). These miRNA (i.e., miR-122, miR-146) are also associated with downregulation of mRNA such as DDR2 and IGFBP6, respectively. These findings suggest integrated interactions between miRNAs and target mRNAs in AKI-related processes such as inflammation, immune cell activation and epithelial-to-mesenchymal transition. These data contribute several novel findings when describing the epigenetic regulation of AKI by miRNA, and also underscores the importance of utilizing an appropriate reference control tissue to understand canonical pathway alterations in AKI.
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Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes

Wireless traffic prediction is a fundamental enabler to proactive network optimisation in beyond 5G. Forecasting extreme demand spikes and troughs due to traffic mobility is essential to avoiding outages and improving energy efficiency. Current state-of-the-art deep learning forecasting methods predominantly focus on overall forecast performance and do not offer probabilistic uncertainty quantification (UQ). Whilst Gaussian Process (GP) models have UQ capability, it is not able to predict extreme values very well. Here, we design a feature embedding (FE) kernel for a GP model to forecast traffic demand with extreme values. Using real 4G base station data, we compare our FE-GP performance against both conventional naive GPs, ARIMA models, as well as demonstrate the UQ output. For short-term extreme value prediction, we demonstrated a 32\% reduction vs. S-ARIMA and 17\% reduction vs. Naive-GP. For long-term average value prediction, we demonstrated a 21\% reduction vs. S-ARIMA and 12\% reduction vs. Naive-GP. The FE kernel also enabled us to create a flexible trade-off between overall forecast accuracy against peak-trough accuracy. The advantage over neural network (e.g. CNN, LSTM) is that the probabilistic forecast uncertainty can inform us of the risk of predictions, as well as the full posterior distribution of the forecast.
Conservative-coordinate transformations for atmospheric measurements

This lecture describes a technique by which atmospheric measurements of trace species with medium to long lifetimes can be 'coincidentally' compared and validated even though measurements are taken at different locations and different times In other words, the method, under suitable counditions, can remove a large amount of the natural meteorological variability. The technique involves the use of quasi-Lagrangian or conservative coordinates - air parcel tags which are invariant or nearly invariant under the motion of the parcel. The technique is called 'reconstruction', since measurements taken at one location can be reconstructed at different locations. In order to do this, the data are transformed into the conservative coordinates and accumulated. Within the conservative reference frame, much of the meteorological variability is removed. Once enough data are obtained within the system, the observations can be tranformed back into physical space at any location and compared with other measurements. The method by which the trace species data are obtained makes no difference; satellite, balloon, rocket, aircraft and ground-based data all become equivalent and can be intercompared. The conservative-coordinate system not only allows for intercomparison of data, but shows how data can be taken in such a way as to maximize the physical scope of the information. In other words, the method automatically suggests when conditions might be suitable to obtain information with different environmental situations. It also allows for the forecast of constituent fields using only the meteorological forecasts and limited observational data.
Measuring Short-and Long-run Promotional ffectiveness on Scanner Data Using Persistence Modeling

The use of price promotions to stimulate brand and firm performance is increasing. We discuss how (i) the availability of longer scanner data time series, and (ii) persistence modeling, have lead to greater insights into the dynamic effects of price promotions, as one can now quantify their immediate, short-run, and long-run effectiveness. We review recent methodological developments, and illustrate how the analysis of numerous brands and product categories has resulted in various empirical generalizations. Finally, we argue that persistence modeling should not only be applied to traditional performance metrics such as sales, but also to metrics such as firm value and customer equity. Introduction Consumers are confronted with all kinds of promotional activities when visiting various retail outlets such as supermarkets. Indeed, temporary price cuts, features, and displays seem to be omni-present. Recent figures (see e.g., Steenkamp et al. [1]) indicate that 24% of all purchases in Dutch supermarkets take place under some form of promotional support. Comparable numbers are observed in the United Kingdom and Spain, while in the United States, this number approaches 40%. Price promotions are the most often used form of promotional support. As such, it should come as no surprise that the effectiveness of price promotions has been studied extensively in the marketing literature (see e.g., [2], [3]). Promotional-effectiveness research has been facilitated through the advent of scanner data. Initially, scanner data offered a major impetus to cross-sectional research, in particular the study of heterogeneity in consumer response to price promotions. This heterogeneity has been studied at the level of brand choice, purchase quantity, and category incidence (see Pauwels, Hanssens and Siddarth [4, Table 1] for a recent review). Multinomial logit and probit models have been the most frequently used modeling approaches in this respect (cf. Franses and Paap [5]). As longer scanner time series became available, an interest emerged in using these data sources to make inferences on price promotions’ over-time impact, and to separate immediate from short-run and even long-run effectiveness. A number of research streams that deal with this issue have emerged. Mela, Gupta, and Lehmann [6] and Papatla and Krishnamurthi [7], among others, incorporate standard dynamic specifications such as the Koyck model (see [6]) into individual-choice logit or probit models. While these methods are appropriate to study dynamic consumer response in stable markets, where constant means and variances in performance and marketing support have already been established, they are not well suited in evolving, or
Understanding the partitioning of rainfall by the maize canopy through computational modelling and physical measurements

The interception and redirection of rainfall by vegetation has implications for many fields such as remote sensing of soil moisture, satellite observation of rainfall, and the modeling of runoff, climate, and soil erosion. Although the modeling of rainfall partitioning by forests has received attention in the past, partitioning caused by crops has been overlooked. The present work proposes a two front experimental and computational methodology to comprehensively study rainfall interception and partitioning by the maize canopy. In the experimental stage, we deployed two compact weather stations, two optical disdrometers, and five tipping bucket rain gauges. Two of the tipping bucket rain gauges were modified to measure throughfall while two were adapted to measure stemflow. The first optical disdrometer allowed for inspection of the unmodified drop-size and velocity distributions, whereas the second disdrometer measured the corresponding distributions under the canopy. This indicates that the outcome of the interaction between the hydrometeors and the canopy depends on the drop diameter. In the computational stage, we created a model that uses drop-size and velocity distributions as well as a three-dimensional digital canopy to simulate the movement of raindrops on the surfaces of leaves. Our model considers interception, redirection, retention, coalescence, breakup, and re-interception of drops to calculate the stemflow, throughfall, and equivalent height of precipitation stored on plants for a given storm. Moreover, the throughfall results are presented as two-dimensional matrices, where each term corresponds to the accumulated volume of drops that dripped at a given location. This allows insight into the spatial distribution of throughfall beneath the foliage. Finally, we examine the way in which the maize canopy modifies the drop-size distribution by recalculating the drop velocity based on the raindrop’s size and