This article explores how Emmanuel Levinas’s concept of the epiphany of the face can illuminate ethical responsibility in pedagogical encounters under late-modern conditions, where masks and roles dominate intersubjective life. Drawing on a normative-philosophical reading of Levinas and contextualised by sociological diagnoses, the analysis foregrounds how ethical responsibility precedes method, rule, and moral codes. A simple heuristic triad—the lived, the emotional, and the vulnerable—makes visible what masks attempt to conceal yet persistently leak through our (micro-)gestures. The article further examines how “the Third” translates the primary ethical call into justice and institutions without dissolving its asymmetry, and how Levinas’s account of language shapes pedagogical communication. The contribution is conceptual: it articulates conditions for pedagogical judgement and responsibility, and points toward implications for assessment, professional formation, and the design of pedagogical frameworks.
Philosophy (General), Theory and practice of education
Mohammad Amin Hemmati, Marzieh Monemi, Shima Asli
et al.
The gut microbiota significantly impacts human health, influencing metabolism, immunological responses, and disease prevention. Dysbiosis, or microbial imbalance, is linked to various diseases, including cancer. It is crucial to preserve a healthy microbiome since pathogenic bacteria, such as Escherichia coli and Fusobacterium nucleatum, can cause inflammation and cancer. These pathways can lead to the formation of tumors. Recent advancements in high-throughput sequencing, metagenomics, and machine learning have revolutionized our understanding of the role of gut microbiota in cancer risk prediction. Early detection is made easier by machine learning algorithms that improve the categorization of cancer kinds based on microbiological data. Additionally, the investigation of the microbiome has been transformed by next-generation sequencing (NGS), which has made it possible to fully profile both cultivable and non-cultivable bacteria and to understand their roles in connection with cancer. Among the uses of NGS are the detection of microbial fingerprints connected to treatment results and the investigation of metabolic pathways implicated in the development of cancer. The combination of NGS with machine learning opens up new possibilities for creating customized medicine by enabling the development of diagnostic tools and treatments that are specific to each patient’s microbiome profile, even in the face of obstacles like data complexity. Multi-omics studies reveal microbial interactions, biomarkers for cancer detection, and gut microbiota’s impact on cancer progression, underscoring the need for further research on microbiome-based cancer prevention and therapy.
Snow avalanches present significant risks to human life and infrastructure, particularly in mountainous regions, making effective monitoring crucial. Traditional monitoring methods, such as field observations, are limited by accessibility, weather conditions, and cost. Satellite-borne Synthetic Aperture Radar (SAR) data has become an important tool for large-scale avalanche detection, as it can capture data in all weather conditions and across remote areas. However, traditional processing methods struggle with the complexity and variability of avalanches. This chapter reviews the application of deep learning for detecting and segmenting snow avalanches from SAR data. Early efforts focused on the binary classification of SAR images, while recent advances have enabled pixel-level segmentation, providing greater accuracy and spatial resolution. A case study using Sentinel-1 SAR data demonstrates the effectiveness of deep learning models for avalanche segmentation, achieving superior results over traditional methods. We also present an extension of this work, testing recent state-of-the-art segmentation architectures on an expanded dataset of over 4,500 annotated SAR images. The best-performing model among those tested was applied for large-scale avalanche detection across the whole of Norway, revealing important spatial and temporal patterns over several winter seasons.
This study develops a cybernetically inspired mixed-methods framework that bridges the gap between policy formation and implementation through feedback-driven analysis of mobility transitions. Using a major campus consolidation in Trondheim, Norway as a case study, we examine how this framework supports sustainable mobility through integrated analysis of mobility patterns, constraints, and transition impacts. The consolidation eliminates over 1,300 parking spaces while increasing daily population by 9,300 people. We employ a mixed-methods approach combining qualitative survey data (n=573) with quantitative big data analysis of public transit and crowd movement patterns. This integrates three analytical components and provides grounded insights into commuting flows, modes, durations, distances, and congestion points, while addressing the spatiotemporal mobility realities of affected populations. The analysis reveals complex mobility constraints, with 59.3% of respondents having children and private cars dominating in winter (49.4%). Though there is broad support for sustainable mobility goals, 86.0% identify increasing travel duration as primary difficulty. Quantitative analysis highlights peak usage patterns and congestion risks, with seasonal variations. This study demonstrates integrating qualitative and quantitative analysis to anticipate negative impacts and enable efficient sustainable mobility policies. The results inform practical recommendations for data-driven mobility interventions that align sustainability goals with lived realities.
The dense and distributed deployment of sub-THz radio units (RUs) alongside sub-10 GHz access point (AP) is a promising approach to provide high data rate and reliable coverage for future 6G applications. However, beam search or RU selection for the sub-THz RUs incurs significant overhead and high power consumption. To address this, we introduce a method that leverages deep learning to infer a suitable sub-THz RU candidate from a set of sub-THz RUs using the sub-10 GHz channel characteristics. A novel aspect of this work is the consideration of inter-band beam configuration (IBBC), defined as the broadside angle between the low-band and high-band antenna patterns of the user equipment (UE). Since IBBC indicates the beamforming information or UE's orientation, it is typically not shared with the network as a part of signalling. Therefore, we propose a solution strategy to infer a suitable sub-THz RU even when UEs do not share their IBBC information. Simulation results illustrate the performance of the inferred sub-THz RU and highlights the detrimental impact of neglecting UE orientation on the systems performance.
Mohammad Mirzaahmadi, Hamed Sarkardeh, Ali Katebi
et al.
This study identifies 35 risks related to the supply chain of concrete dams, of which 25 were selected as critical risks using the Delphi method. Risks were categorized into three groups based on their sources including environmental, network and organizational risks. The Failure Mode and Effect Analysis (FMEA) method was then applied to evaluate the significance of risks in three areas of construction cost, time and quality. The results revealed that 44% of risks in the construction cost category were critical, while 21% of risks in construction time and 12% in construction quality were found to be critical. Appropriate responses to mitigate critical risks were provided, including the use of alternative supply resources, rigorous project scheduling, and diversified financial strategies. This study highlights the importance of risk management in improving project performance in terms of cost, time, and quality, and emphasizes the need for effective communication and continuous risk monitoring throughout the project lifecycle.
While the huge data repositories of web archives carry big potential for knowledge production in academia, researchers have described significant challenges when trying to access and make use of web archives in research. This article describes the creation of a “Web News Collection” where content from the National Library of Norway’s web archive has been made available for computational text analysis, in a manner that facilitates access for research and beyond – aligning with FAIR principles, while also accounting for copyright restrictions. Developing the warc2corpus pipeline, we detail the processes for extracting natural language from WARC files, curating content, and enhancing metadata for analytical purposes. This structured collection — consisting of 1.5 million news articles accessible via a REST API —enables distant reading of news from the web, with tools for building corpora, word frequencies and collocations. To support usage, both programming interfaces and user-friendly web apps are offered, representing a significant step forward in making web archives usable and valuable for digital scholars.
History of scholarship and learning. The humanities, Language and Literature
Jon Brage Svenning, Terje Vasskog, Karley Campbell
et al.
The diatom lipidome actively regulates photosynthesis and displays a high degree of plasticity in response to a light environment, either directly as structural modifications of thylakoid membranes and protein–pigment complexes, or indirectly via photoprotection mechanisms that dissipate excess light energy. This acclimation is crucial to maintaining primary production in marine systems, particularly in polar environments, due to the large temporal variations in both the intensity and wavelength distributions of downwelling solar irradiance. This study investigated the hypothesis that Arctic marine diatoms uniquely modify their lipidome, including their concentration and type of pigments, in response to wavelength-specific light quality in their environment. We postulate that Arctic-adapted diatoms can adapt to regulate their lipidome to maintain growth in response to the extreme variability in photosynthetically active radiation. This was tested by comparing the untargeted lipidomic profiles, pigmentation, specific growth rates and carbon assimilation of the Arctic diatom Porosira glacialis vs. the temperate species Coscinodiscus radiatus during exponential growth under red, blue and white light. Here, we found that the chromatic wavelength influenced lipidome remodeling and growth in each strain, with P. glacialis showing effective utilization of red light coupled with increased inclusion of primary light-harvesting pigments and polar lipid classes. These results indicate a unique photoadaptation strategy that enables Arctic diatoms like P. glacialis to capitalize on a wide chromatic growth range and demonstrates the importance of active lipid regulation in the Arctic light environment.
Simone Casolo, Alexander Stasik, Zhenyou Zhang
et al.
We present an investigation of how topological data analysis (TDA) can be applied to condition-based monitoring (CBM) of wind turbines for energy generation. TDA is a branch of data analysis focusing on extracting meaningful information from complex datasets by analyzing their structure in state space and computing their underlying topological features. By representing data in a high-dimensional state space, TDA enables the identification of patterns, anomalies, and trends in the data that may not be apparent through traditional signal processing methods. For this study, wind turbine data was acquired from a wind park in Norway via standard vibration sensors at different locations of the turbine's gearbox. Both the vibration acceleration data and its frequency spectra were recorded at infrequent intervals for a few seconds at high frequency and failure events were labelled as either gear-tooth or ball-bearing failures. The data processing and analysis are based on a pipeline where the time series data is first split into intervals and then transformed into multi-dimensional point clouds via a time-delay embedding. The shape of the point cloud is analyzed with topological methods such as persistent homology to generate topology-based key health indicators based on Betti numbers, information entropy and signal persistence. Such indicators are tested for CBM and diagnosis (fault detection) to identify faults in wind turbines and classify them accordingly. Topological indicators are shown to be an interesting alternative for failure identification and diagnosis of operational failures in wind turbines.
The Northern European Enclosure Dam (NEED) is a hypothetical project to prevent flooding in European countries following the rising ocean level due to melting arctic glaciers. This project involves the construction of two large dams between Scotland and Norway, as well as England and France. The anticipated cost of this project is 250 to 500 billion euros. In this paper, we present the simulation of the aftermath of flooding on the European coastline caused by a catastrophic break of this hypothetical dam. From our simulation results, we can observe that there is a traveling wave after the accident, with a velocity of around 10 kilometers per hour, raising the sea level permanently inside the dammed region. This observation implies a need to construct additional dams or barriers protecting the northern coastline of the Netherlands and the interior of the Baltic Sea. Our simulations have been obtained using the following building blocks. First, a graph transformation model was applied to generate an adaptive mesh approximating the topography of the Earth. We employ the composition graph grammar model for breaking triangular elements in the mesh without the generation of hanging nodes. Second, the wave equation is formulated in a spherical latitude-longitude system of coordinates and solved by a high-order time integration scheme using the generalized $α$ method.
Trond Brandvik, Louis Gosselin, Zhaohui Wang
et al.
Refractory flue walls in anode baking furnaces are exposed to harsh conditions during operation, affecting the structural properties of the material. The flue walls in industrial furnaces degrade over time to the point where they no longer perform as intended and must be replaced. Earlier studies of spent refractory lining from anode baking furnaces have shown considerable densification of the flue wall bricks, where the densification varies significantly from the anode side to the flue side of the brick. The observed densification is proposed to be caused by high-temperature creep, and the aim of this work was to determine whether the uneven densification across the brick could be modeled using a finite element method (FEM) implementing high-temperature steady-state creep. Finite element modeling was used to model steady-state creep for a material similar to that used in the baking furnace. Thermal and physical parameters and boundary conditions were chosen to simulate the conditions in an anode baking furnace. Refractory samples of pristine and spent lining from the baking furnace were also analyzed with X-ray computed tomography (CT), with a reduction in the porosity confirming the densification during operation. The FEM modeling demonstrated that high-temperature creep could explain the observed densification in the spent flue walls. The present findings may be useful in relation to increasing the lifetime of industrial flue walls.
Temperature-induced disasters lead to major human and economic damage, but the relationship between their climatic drivers and impacts is difficult to quantify. In part, this is due to a lack of data with suitable resolution, scale and coverage on impacts and disaster occurrence. Here, we address this gap using new datasets on subnational sector-disaggregated economic productivity and geo-coded disaster locations to quantify the role of climatic hazards on economic impacts of temperature-induced disasters at a subnational scale. Using a regression-based approach, we find that the regional economic impacts of heat-related disasters are most strongly linked to the daily maximum temperature (TXx) index. This effect is largest in the agricultural sector (6.37% regional growth rate reduction per standard deviation increase in TXx anomaly), being almost twice as strong as in the manufacturing sector (3.98%), service sector (3.64%), and whole economy (3.64%). We also highlight the role of compound climatic hazards in worsening impacts, showing that in the agriculture sector, compound hot-and-dry conditions amplify the impacts of heat-related disasters on growth rates by a factor of two. In contrast, in the service and manufacturing sectors, stronger impacts are found to be associated with compound hot and wet conditions. These findings present a first step in understanding the relationship between temperature-related hazards and regional economic impacts using a multi-event database, and highlight the need for further research to better understand the complex mechanisms including compound effects underlying these impacts across sectors.
Elin Halvorsen, Hans A Holter, Serdar Ozkan
et al.
Abstract This paper examines whether nonlinear and non-Gaussian features of earnings dynamics are caused by hours or hourly wages. Our findings from the Norwegian administrative and survey data are as follows: (i) Nonlinear mean reversion in earnings is driven by the dynamics of hours worked rather than wages since wage dynamics are close to linear, while hours dynamics are nonlinear—negative changes to hours are transitory, while positive changes are persistent. (ii) Large earnings changes are driven equally by hours and wages, whereas small changes are associated mainly with wage shocks. (iii) Both wages and hours contribute to negative skewness and high kurtosis for earnings changes, although hour-wage interactions are quantitatively more important. (iv) When considering household earnings and disposable household income, the deviations from normality are mitigated relative to individual labor earnings: changes in disposable household income are approximately symmetric and less leptokurtic.
Anja Vaskinn, Jaroslav Rokicki, Christina Bell
et al.
Abstract Background and Hypothesis Reduced social cognition has been reported in individuals who have committed interpersonal violence. It is unclear if individuals with schizophrenia and a history of violence have larger impairments than violent individuals without psychosis and non-violent individuals with schizophrenia. We examined social cognition in two groups with violent offenses, comparing their performance to non-violent individuals with schizophrenia and healthy controls. Study Design Two social cognitive domains were assessed in four groups: men with a schizophrenia spectrum disorder with (SSD-V, n = 27) or without (SSD-NV, n = 42) a history of violence, incarcerated men serving preventive detention sentences (V, n = 22), and healthy male controls (HC, n = 76). Theory of mind (ToM) was measured with the Movie for the Assessment of Social Cognition (MASC), body emotion perception with Emotion in Biological Motion (EmoBio) test. Study Results Kruskal–Wallis H-tests revealed overall group differences for social cognition. SSD-V had a global and clinically significant social cognitive impairment. V had a specific impairment, for ToM. Binary logistic regressions predicting violence category membership from social cognition and psychosis (SSD status) were conducted. The model with best fit, explaining 18%–25% of the variance, had ToM as the only predictor. Conclusions Social cognitive impairment was present in individuals with a history of violence, with larger and more widespread impairment seen in schizophrenia. ToM predicted violence category membership, psychosis did not. The results suggest a role for social cognition in understanding interpersonal violence.
Climate-related targets for cities abound, but it is unclear how important they are in driving actual transformations. Scholars have often taken a skeptical view of official climate discourses, including their ambitious targets, and instead turned their attention to experimentation, innovation and civic action – colloquially termed 'real action.' In this article we try on the opposite view. Contributing to 'speculative political ecology', we argue that climate-related targets, even those without hard policies directly attached to them, can render climate change more governable and actionable. In a fragmented, polycentric and dispersed governance landscape, the immutability of a 'hard' number can create coherence, direction and measurability to policy action. We examine a particular target, and its associated governance instruments, which has arguably had a transformative effect on urban policy. Our empirical focus is Norway's Zero Growth Objective in urban transport policy. We follow the target from its first formulation as a soft goal around 2006 and until 2019, by when it had materialized as a hard target shaping funding streams and concrete policy interventions, and most likely, emission levels. Arguably, it has been a highly effective frame for policy.
Abstract The Dead Sea Fault (DSF) is a crustal‐scale continental transform fault separating the African and the Arabian plates. Neogene to Quaternary volcanic activity is well‐spread in Northern Israel. Yet, the origin of the magmas that fed the eruptions is still unpinned. Our local earthquake tomography depicts velocity distributions typical of rifting settings. At 9 km depth, a prominent high Vp/Vs anomaly marks the presence of cooling melts. We propose that protracted transtension along the DSF caused crustal thinning promoting the emplacement of magmatic bodies. Crustal emplacements of magmas in Northern Israel reconcile multiple observations, including the high geothermal gradient, the prominent magnetic anomalies and the traces of mantle‐derived fluids in the springs across the Sea of Galilee. We provide a compelling evidence for rifting in segments of the DSF and identify the potential source of magmatism that fed part of the volcanic activity of the area.
The complexity of delivering business value is increasing technically and socially. The increasing complexity triggers the need for an increase in systems competence in several roles within the technical domain. One of the core disciplines to focus on this competence is systems engineering, which gets increasing attention within the Dutch ecosystem to enhance individuals and organizations further in this competence. The challenge is a shortage of systems engineers and teachers in systems engineering. This study proposes a layered and integrated education offering with courses for depth and domain skills, multi-day programs with systems mindset and leadership capabilities, and tracks to broaden the knowledge to a broad variety of stakeholders. In addition, university colleges, universities, and other education providers have to cooperate in delivering cohesive education to all levels, e.g., bachelor, master, PhD, and lifelong learning.
Kaja Elisabeth Nilsen, Astrid Skjesol, June Frengen Kojen
et al.
Toll-like receptor 8 (TLR8) recognizes single-stranded RNA of viral and bacterial origin as well as mediates the secretion of pro-inflammatory cytokines and type I interferons by human monocytes and macrophages. TLR8, as other endosomal TLRs, utilizes the MyD88 adaptor protein for initiation of signaling from endosomes. Here, we addressed the potential role of the Toll-interleukin 1 receptor domain-containing adaptor protein (TIRAP) in the regulation of TLR8 signaling in human primary monocyte-derived macrophages (MDMs). To accomplish this, we performed TIRAP gene silencing, followed by the stimulation of cells with synthetic ligands or live bacteria. Cytokine-gene expression and secretion were analyzed by quantitative PCR or Bioplex assays, respectively, while nuclear translocation of transcription factors was addressed by immunofluorescence and imaging, as well as by cell fractionation and immunoblotting. Immunoprecipitation and Akt inhibitors were also used to dissect the signaling mechanisms. Overall, we show that TIRAP is recruited to the TLR8 Myddosome signaling complex, where TIRAP contributes to Akt-kinase activation and the nuclear translocation of interferon regulatory factor 5 (IRF5). Recruitment of TIRAP to the TLR8 signaling complex promotes the expression and secretion of the IRF5-dependent cytokines IFNβ and IL-12p70 as well as, to a lesser degree, TNF. These findings reveal a new and unconventional role of TIRAP in innate immune defense.
The race for road electrification has started, and convincing drivers to switch from fuel-powered vehicles to electric vehicles requires robust Electric Vehicle (EV) charging infrastructure. This article proposes an innovative EV charging demand estimation and segmentation method. First, we estimate the charging demand at a neighborhood granularity using cellular signaling data. Second, we propose a segmentation model to partition the total charging needs among different charging technology: normal, semi-rapid, and fast charging. The segmentation model, an approach based on the city's points of interest, is a state-of-the-art method that derives useful trends applicable to city planning. A case study for the city of Brussels is proposed.
Odin Foldvik Eikeland, Finn Dag Hovem, Tom Eirik Olsen
et al.
The energy market relies on forecasting capabilities of both demand and power generation that need to be kept in dynamic balance. Today, when it comes to renewable energy generation, such decisions are increasingly made in a liberalized electricity market environment, where future power generation must be offered through contracts and auction mechanisms, hence based on forecasts. The increased share of highly intermittent power generation from renewable energy sources increases the uncertainty about the expected future power generation. Point forecast does not account for such uncertainties. To account for these uncertainties, it is possible to make probabilistic forecasts. This work first presents important concepts and approaches concerning probabilistic forecasts with deep learning. Then, deep learning models are used to make probabilistic forecasts of day-ahead power generation from a wind power plant located in Northern Norway. The performance in terms of obtained quality of the prediction intervals is compared for different deep learning models and sets of covariates. The findings show that the accuracy of the predictions improves when historical data on measured weather and numerical weather predictions (NWPs) were included as exogenous variables. This allows the model to auto-correct systematic biases in the NWPs using the historical measurement data. Using only NWPs, or only measured weather as exogenous variables, worse prediction performances were obtained.