Through my work as an artist, educator, and activist, I aim to promote the interconnectedness of arts, management, creativity, curating, research, and technology by developing courses for art academies, art students, and interdisciplinary students at universities. I believe that future university and art academy education will increasingly integrate interdisciplinarity, combining fields such as management, creativity, technology (including artificial intelligence), curating, and other disciplines. This approach aligns with the interdisciplinary nature of professional life after graduation, whether one pursues a career as an artist or as a director of an institution. I reference both the art academy and university environments due to my experience as a lecturer in both settings. The article explores the relationship between authentic leadership and creativity within the context of university pedagogy, utilizing a reflexive research method based on a case study of creativity education. This case study focuses solely on the university environment, specifically on teaching creativity to interdisciplinary groups of students. My experience as a lecturer contains teaching artists, performers, and interdisciplinary students at both the Bachelor of Arts and Master of Arts levels. Data includes my notes as a facilitator-lecturer, as well as the creative output and feedback from the interdisciplinary student groups. This data is analyzed using qualitative methods. The case study, derived from a Digital Creativity course, is examined speculatively to assess how the skills acquired may impact students post-graduation. As I was not in contact with students after the course ended, observations are limited to the processes observed during the course itself. The research question addressed is: how can digital learning of authentic leadership and creativity be facilitated within university pedagogy to create a safe and empathetic learning environment for students? Leadership is discussed from the perspective of authentic leadership and its relationship to creativity. Digital literacy is framed as critical thinking and responsibility in the context of collaboration with non-human entities, such as artificial intelligence. This study is relevant for university and art academy professionals working in cross-disciplinary contexts related to authentic leadership and creativity.
Sabine Leske, Tor Endestad, Vegard Volehaugen
et al.
Abstract Periodic sensory inputs entrain oscillatory brain activity, reflecting a neural mechanism that might be fundamental to temporal prediction and perception. Most environmental rhythms and patterns in human behavior, such as walking, dancing, and speech do not, however, display strict isochrony but are instead quasi-periodic. Research has shown that neural tracking of speech is driven by modulations of the amplitude envelope, especially via sharp acoustic edges, which serve as prominent temporal landmarks. In the same vein, research on rhythm processing in music supports the notion that perceptual timing precision varies systematically with the sharpness of acoustic onset edges, conceptualized in the beat bin hypothesis. Increased envelope sharpness induces increased precision in localizing a sound in time. Despite this tight relationship between envelope shape and temporal processing, it is currently unknown how the brain uses predictive information about envelope features to optimize temporal perception. With the current EEG study, we show that the predicted sharpness of the amplitude envelope is encoded by pre-target neural activity in the beta band (15–25 Hz), and has an impact on the temporal perception of target sounds. We used probabilistic sound cues in a timing judgment task to inform participants about the sharpness of the amplitude envelope of an upcoming target sound embedded in a beat sequence. The predictive information about the envelope shape modulated task performance and pre-target beta power. Interestingly, these conditional beta-power modulations correlated positively with behavioral performance in the timing judgment task and with perceptual temporal precision in a click-alignment task. This study provides new insight into the neural processes underlying prediction of the sharpness of the amplitude envelope during beat perception, which modulate the temporal perception of sounds. This finding could reflect a process that is involved in temporal prediction, exerting top-down control on neural entrainment via the prediction of acoustic edges in the auditory stream.
With rising concerns about climate change, the issue of green growth have been growing in importance. The aim of this study is to establish a measurement method for green growth, and to identify the best performing countries in this field. The Future, Outside, and Inside (FOI) development model was used to measure the performance of the 38 OECD countries. Based on their 2019-20 scores, the countries that are top performers in green growth are the members of the so called Welfare-participatory cluster (Austria, Denmark, Finland, Germany, Ireland, Isra-el, New Zealand, Norway, Sweden), and two outliers (Iceland, and Luxembourg).
Manudeep Bhuller, Lukas Delgado-Prieto, Santiago Hermo
et al.
This paper investigates how institutional wage-setting constraints, such as a national minimum wage or collectively bargained wages, affect firm responses to demand shocks. We develop a framework to interpret heterogeneous shock responses that depend on the constraints firms face, and provide empirical evidence on the relevance of these constraints in shaping firm behavior across three countries with different institutional settings: Portugal, Norway, and Colombia. We discuss the implications of our findings for conventional estimates of rent-sharing and employer wage-setting power.
Mohammad Nooraiepour, Pawel Gladysz, Eirik Melaaen
In the face of escalating climate change, achieving significant reductions in greenhouse gas emissions from hard-to-abate industrial sectors is imperative. Carbon Capture and Storage (CCS) represents an essential technological advancement to achieve sustainable decarbonization. This manuscript reports on the bilateral CCS network between Norway and Poland, designed and implemented to leverage their capabilities to expedite technology deployment via mutual cooperation and accelerate CCS initiatives targeted to member states' challenges across Europe, aiming for a meaningful contribution to climate goals. Norway is renowned for its operational acumen, as demonstrated by landmark projects like Sleipner and Snohvit, and its forward-looking initiatives, such as the open-source cross-border Northern Lights project, which offer advanced infrastructure and expertise. Conversely, Poland, characterized by its coal-dependent economy and the challenge of decarbonizing extensive industrial emissions, presents a significant geological CO2 storage potential, estimated at over 15.5 gigatonnes. This study delves into the potential synergies derived from collaborative endeavors in academic education, research and development, industrial implementation, regulatory coherence, and public engagement. By underscoring the reciprocal benefits of such partnership, the study underscores the indispensable role of bilateral cooperation in harnessing CCS's capabilities to meet the EU's ambitious climate objectives, paving the way toward a sustainable and low-carbon future. Additionally, it outlines a scalable model for fostering and supporting broader bilateral and multi-lateral collaborations, emphasizing the pivotal role of interconnected networks in shaping effective global climate action strategies.
Karina Lilleborge, Sara Martino, Geir-Arne Fuglstad
et al.
Metric graphs are useful tools for describing spatial domains like road and river networks, where spatial dependence act along the network. We take advantage of recent developments for such Gaussian Random Fields (GRFs), and consider joint spatial modelling of observations with different spatial supports. Motivated by an application to traffic state modelling in Trondheim, Norway, we consider line-referenced data, which can be described by an integral of the GRF along a line segment on the metric graph, and point-referenced data. Through a simulation study inspired by the application, we investigate the number of replicates that are needed to estimate parameters and to predict unobserved locations. The former is assessed using bias and variability, and the latter is assessed through root mean square error (RMSE), continuous rank probability scores (CRPSs), and coverage. Joint modelling is contrasted with a simplified approach that treat line-referenced observations as point-referenced observations. The results suggest joint modelling leads to strong improvements. The application to Trondheim, Norway, combines point-referenced induction loop data and line-referenced public transportation data. To ensure positive speeds, we use a non-linear link function, which requires integrals of non-linear combinations of the linear predictor. This is made computationally feasible by a combination of the R packages inlabru and MetricGraph, and new code for processing geographical line data to work with existing graph representations and fmesher methods for dealing with line support in inlabru on objects from MetricGraph. We fit the model to two datasets where we expect different spatial dependency and compare the results.
Using data from the Longyearbyen weather station, quantile gradient boosting (``small AI'') is applied to forecast daily 2023 temperatures in Svalbard, Norway. The 0.60 quantile loss weights underestimates about 1.5 times more than overestimates. Predictors include five routinely collected indicators of weather conditions, each lagged by 14~days, yielding temperature forecasts with a two-week lead time. Conformal prediction regions quantify forecasting uncertainty with provably valid coverage. Forecast accuracy is evaluated with attention to local stakeholder concerns, and implications for Arctic adaptation policy are discussed.
This Data Descriptor introduces the dataset Enevaeldens Nyheder Online (News during Absolutism Online). The Enevaeldens Nyheder Online (ENO) dataset provides a reconstruction of the contents of major newspapers in Denmark and Norway during the period of Absolutism (1660-1849). The dataset contains approx. 474 million words, created using neural networks designed to process digitised microfilm versions of Danish newspapers as well as a smaller selection of Norwegian publications that were all hitherto illegible for computers. The contributions details this process and its results, including a way to derive standalone texts from the editions, and the accompanying BERT-model trained on a beta-version of the dataset.
Elisabeth Valmyr Bania, Toril Sørheim Nilsen, Mikael Thastum
et al.
IntroductionSchool absenteeism represents a concern for students, educators, and parents alike. Teachers’ involvement is vital to students’ school life. Consequently, integrating schools and teachers effectively in absenteeism interventions is of great importance. However, few studies have investigated teachers’ perspectives on participating in manual-based, indicated interventions to promote school attendance. This study aimed to explore teachers’ experiences with the manual-based Back2School (B2S) intervention, which is based on cognitive behavioural therapy (CBT).MethodsSeven primary and lower secondary school teachers agreed to participate in individual interviews following their involvement in the intervention. These teachers engaged in various aspects of the intervention, including data collection, school sessions, and school meetings involving students, parents, and B2S group leaders.ResultsThe results indicate that some of the informants experienced increased competence and self-efficacy regarding school absenteeism following the intervention, while other informants did not have this experience.DiscussionThere is a need for more clarity and enhanced teacher involvement in future B2S interventions.
High-harmonic upconversion driven by a mid-infrared femtosecond laser can generate coherent soft x-ray beams in a tabletop-scale setup. Here, we report on a compact ytterbium-pumped optical parametric chirped pulse amplifier (OPCPA) laser system seeded by an all-fiber front-end and employing periodically poled lithium niobate (PPLN) nonlinear media operated near the pulse fluence limits of current commercially available PPLN crystals. The OPCPA delivers 3 µm wavelength pulses with 775 µJ energy at 1 kHz repetition rate, with transform-limited 120 fs pulse duration, diffraction-limited beam quality, and ultrahigh 0.33% rms energy stability over >18 h. Using this laser, we generate soft x-ray high harmonics (HHG) in argon gas by focusing into a low-loss, high-pressure gas-filled anti-resonant hollow core fiber (ARHCF), generating coherent light at photon energies up to the argon L-edge (250 eV) and carbon K-edge (284 eV), with high beam quality and ∼1% rms energy stability. This work demonstrates soft x-ray HHG in a high-efficiency guided-wave phase matched geometry, overcoming the high losses inherent to mid-IR propagation in unstructured waveguides, or the short interaction lengths of gas cells or jets. The ARHCF can operate in the long term without damage and with the repetition rate, stability, and robustness required for demanding applications in spectromicroscopy and imaging. Finally, we discuss routes for further optimizing the soft x-ray HHG flux by driving He at higher laser intensities using either the signal (1.5 μm) or idler wavelengths (3 μm).
We develop a model that captures peer effect heterogeneity by modeling the endogenous spillover to be linear in ordered peer outcomes. Unlike the canonical linear-in-means model, our approach accounts for the distribution of peer outcomes as well as the size of peer groups. Under a minimal condition, our model admits a unique equilibrium and is therefore tractable and identified. Simulations show our estimator has good finite sample performance. Finally, we apply our model to educational data from Norway, finding that higher-performing friends disproportionately drive GPA spillovers. Our framework provides new insights into the structure of peer effects beyond aggregate measures.
Tesfu Fekensa Tujuba, Roman V. Yakovlev, Aidas Saldaitis
et al.
African golden-spotted lappet moths from the genera Haplopacha Aurivillius, 1905 and Dasychirinula Hering, 1926 are reviewed. Antennae, heads and legs are investigated in addition to the traditional comparison of habitus, genitalia, and distribution areas. Two new genera are established and four new species are described as a result: Eudoumbia gen. n. with the type-species Eudoumbia thorogood sp. n. from Angola, Namibia, and Botswana; Auripluvia gen. n. with the type species Auripluvia sophia sp. n. from Ethiopia; Haplopacha mason sp. n. from Tanzania; and Dasychirinula julia sp. n. from Ethiopia. One species is reattributed to the new genus as Eudoumbia ndoumoi (Dupont, Simonsen & Zilli, 2016) stat. n. One new synonymy is established for two Tanzanian species: Dasychirinula chrysogramma Hering, 1926 = Haplopacha lunata Dupont, Simonsen & Zilli, 2016 syn. n.; and one potential synonymy is remarked between Haplopacha tangani Dupont, Simonsen & Zilli, 2016 from Tanzania and Malawi and Haplopacha riftensis Dupont, Simonsen & Zilli, 2016 from Malawi. Photos of the related material stored in the main collections of Ditsong National Museum of Natural History (Pretoria, RSA) and Natural History Museum of Zimbabwe (Bulawayo, Zimbabwe) are showed.
Dimitrios Kleftogiannnis, Sonia Gavasso, Benedicte Sjo Tislevoll
et al.
Summary: Mass cytometry by time-of-flight (CyTOF) is an emerging technology allowing for in-depth characterization of cellular heterogeneity in cancer and other diseases. Unfortunately, high-dimensional analyses of CyTOF data remain quite demanding. Here, we deploy a bioinformatics framework that tackles two fundamental problems in CyTOF analyses namely (1) automated annotation of cell populations guided by a reference dataset and (2) systematic utilization of single-cell data for effective patient stratification. By applying this framework on several publicly available datasets, we demonstrate that the Scaffold approach achieves good trade-off between sensitivity and specificity for automated cell type annotation. Additionally, a case study focusing on a cohort of 43 leukemia patients reported salient interactions between signaling proteins that are sufficient to predict short-term survival at time of diagnosis using the XGBoost algorithm. Our work introduces an automated and versatile analysis framework for CyTOF data with many applications in future precision medicine projects.