Languages, policies and interculturality in Danish Higher Education, 2019-24

A central theme in the Danish debate on international higher education has been the relation between language choice, linguistic practices and multiculturalism in the classroom. Prior to 2019, many scholars assumed that the adoption of English as a teaching medium would support the creation of diverse learning environments that enabled students to develop intercultural competences. In 2019, the Danish government implemented a new policy on international recruitment, requesting that universities reduce their intake of internationals. As a result, programmes previously been registered as ‘International’ became marked as ‘Danish’, requesting from applicants knowledge of the Danish language. In the chapter, we examine the consequences of the 2019 intervention for multilingualism and multiculturalism in Danish HE. Our case is a BA programme in International Studies, which, prior to the 2019 policy change had been characterised by great diversity in terms of student nationality. First, we will look at the policy-scapes affecting a BA programme at the European, national and institutional levels. This leads to a comparison of the linguistic and cultural diversity found in the student cohort in 2019 and 2024, as well as changes we have observed in students’ linguistic practice. We end with a discussion that challenges the initial assumption that internationalisation, diversity and multiculturalism are linked. <br/

A Comprehensive Review of Hybrid Battery State of Charge Estimation: Exploring Physics-Aware AI-Based Approaches

Accurate State of Charge (SOC) estimation in lithium-ion batteries (LiBs) poses significant challenges due to their nonlinear behavior over their lifetime. Establishing a balance between accuracy, robustness, and low implementation complexity remains critical. Over the past decade, numerous studies have aimed to analyze and compare various SOC estimation methods for commercial LiBs. However, there has been a lack of reviews focusing on SOC estimation from a physics-aware AI-based perspective for LiBs. This paper aims to bridge this gap by evaluating various SOC estimation methods, particularly highlighting hybrid approaches that integrate model-based (MB) and artificial intelligence-based (AIB) strategies. These hybrid methods are categorized into Physics-Aware AI-based (PAAIB) methods and AI-enhanced physical models. The research method involves a comprehensive review of existing literature, discussing the fundamental principles of MB and AIB methods and analyzing their strengths and limitations in capturing the complex dynamics of battery behavior. The paper then explores hybrid SOC estimation techniques in depth, examining each category based on various performance metrics. The paper's main contents include a detailed analysis of hybrid SOC estimation techniques, a comparative review of recent studies, and strategic recommendations for future research. The effects of this research highlight the importance of adopting hybrid SOC estimation methods to improve SOC estimation accuracy and robustness in practical applications. The paper concludes with a summary of key findings, emphasizing the necessity of adopting hybrid approaches for improved SOC estimation in LiBs and their critical role in enhancing the reliability and functionality of battery management systems in electric vehicles.</p

Cancer risk with tocilizumab/sarilumab, abatacept, and rituximab treatment in patients with rheumatoid arthritis: a Danish cohort study

Objectives: To investigate cancer risk in rheumatoid arthritis (RA) patients treated with tocilizumab/sarilumab, abatacept, or rituximab compared with those who received tumour necrosis factor inhibitors (TNFi) and compared with biological disease-modifying anti-rheumatic drugs (bDMARD) naïve RA patients.Methods: Nationwide registry-based cohort study of RA patients initiating treatment with tocilizumab/sarilumab, abatacept, rituximab, TNFi, and bDMARD-naive patients their second type of conventional synthetic DMARD (csDMARD). Patients were identified in DANBIO and followed for cancer from 2006-2020. Patients could contribute multiple treatments, with person years (PYRS), deaths, and cancers allocated to each treatment group in a 'latest type of treatment' manner. Inverse probability of treatment weighting and weighted cause-specific Cox models were used to calculate hazard ratios (HRs) for cancer in each tocilizumab/sarilumab, abatacept, and rituximab group compared with TNFI and bDMARD naïve groups, respectively.Results: In total, 21 982 treatment initiations, 96 475 PYRS, and 1423 cancers were identified. There were no statistically significant increased HRs for overall cancer in tocilizumab/sarilumab, abatacept, or rituximab treatment groups (HRs ranged from 0.7-1.1). More than five years of abatacept exposure showed a non-significantly increased HR compared with TNFi (HR 1.41, 95% confidence intervals CI 0.74-2.71). For hematological cancers, rituximab treatment showed non-significantly reduced HRs: vs TNFi (HR 0.09; 95%CI 0.00-2.06) and bDMARD-naïve (HR 0.13; 95%CI 0.00-1.89).Conclusion: Treatment with tocilizumab/sarilumab, abatacept, or rituximab in RA patients was not associated with increased risks of cancer compared with TNFi-treated and with bDMARD-naïve RA patients in a real-world setting.Objectives: To investigate cancer risk in RA patients treated with tocilizumab/sarilumab, abatacept or rituximab compared with those who received TNF inhibitors (TNFi) and compared with biological DMARDs (bDMARD)-naïve RA patients. Methods: Nationwide registry-based cohort study of RA patients who initiated bDMARD treatment with tocilizumab/sarilumab, abatacept, rituximab, and TNFi, as well as bDMARD-naive patients who initiated their second type of conventional synthetic DMARD. Patients were identified in the Danish Rheumatology Quality Register (DANBIO) and followed for cancer from 2006 to 2020. Patients could contribute multiple treatments, with person years, deaths and cancers allocated to each treatment group in a ‘latest type of treatment’ manner. Inverse probability of treatment weighting and weighted cause-specific Cox models were used to calculate hazard ratios (HRs) for cancer in each tocilizumab/sarilumab, abatacept and rituximab group compared with TNFi-treated and bDMARD-naïve groups, respectively. Results: In total, 21 982 treatment initiations, 96 475 person years and 1423 cancers were identified. There were no statistically significant increased HRs for overall cancer in tocilizumab/sarilumab, abatacept or rituximab treatment groups (HRs ranged from 0.7 to 1.1). More than 5 years of abatacept exposure showed a non-significantly increased HR compared with TNFi (HR 1.41, 95% CI 0.74–2.71). For haematological cancers, rituximab treatment showed non-significantly reduced HRs: vs TNFi-treated (HR 0.09; 95% CI 0.00–2.06) and bDMARD-naïve (HR 0.13; 95% CI 0.00–1.89). Conclusion: Treatment with tocilizumab/sarilumab, abatacept or rituximab in RA patients was not associated with increased risks of cancer compared with TNFi-treated and with bDMARD-naïve RA patients in a real-world setting.</p

Hierarchical online energy management for residential microgrids with Hybrid hydrogen–electricity Storage System

The increasing proportion of renewable energy introduces both long-term and short-term uncertainty to power systems, which restricts the implementation of energy management systems (EMSs) with high dependency on accurate prediction techniques. A hierarchical online EMS (HEMS) is proposed in this paper to economically operate the Hybrid hydrogen–electricity Storage System (HSS) in a residential microgrid (RMG). The HEMS dispatches an electrolyzer-fuel cell-based hydrogen energy storage (ES) unit for seasonal energy shifting and an on-site battery stack for daily energy allocation against the uncertainty from the renewable energy source (RES) and demand side. The online decision-making of the proposed HEMS is realized through two parallel fuzzy logic (FL)-based controllers which are decoupled by different operating frequencies. An original local energy estimation model (LEEM) is specifically designed for the decision process of FL controllers to comprehensively evaluate the system status and quantify the electricity price expectation for the HEMS. The proposed HEMS is independent of RES prediction or load forecasting, and gives the optimal operation for HSS in separated resolutions: the hydrogen ES unit is dispatched hourly and the battery is operated every minute. The performance of the proposed method is verified by numerical experiments fed by real-world datasets. The superiority of the HEMS in expense-saving manner is validated through comparison with PSO-based day-ahead optimization methods, fuzzy logic EMS, and rule-based online EMS.</p

A Two-Stage Deep Learning Approach for Accurate Day-Ahead Electricity Price Forecasting

Participants in the energy market are at greater risk of making decisions due to the nonlinear and volatile characteristics of electricity prices. Accurate short-term electricity price forecasting (EPF) is essential to ensure improved resource allocation, grid stability and enable market participants to manage their decisions efficiently. This study proposes a novel two-stage forecasting framework for day-ahead EPF using time series decomposition methods and hybrid deep learning algorithms. In the first stage, features related to EPF at the next time step are predicted. In this stage, the highest-frequency component extracted via Empirical Mode Decomposition (EMD) is further decomposed using Variational Mode Decomposition (VMD) so as to better capture rapid fluctuations and improve the overall prediction accuracy. Moreover, a decentralized deep learning architecture is designed in which Gated Recurrent Unit (GRU) networks are employed for high-frequency components, while Long Short-term Memory (LSTM) networks are used for the remaining components. In the second stage, EPF is generated using a hybrid LSTM and GRU structure, which incorporates both features estimated in the first stage and historical electricity price data. Finally, hyperparameters of the deep learning models are optimized using Bayesian Optimization to enhance performance. To validate the proposed framework, real market data from the DK1 region of Denmark is used. The proposed hybrid prediction framework is evaluated against both machine learning methods and deep learning-based architectures. Experimental results demonstrate that the proposed method achieves approximately 27.15 % lower RMSE compared to traditional machine learning models, and around 28.24 % lower RMSE compared to LSTM-based models.<br/

Experimental investigations of internal macro-scale convection in the loose-fill wood fiber insulation layer of a full-scale wall element

With increasing restrictions on the energy efficiency of buildings, thicker insulation layers are installed in new and refurbished buildings to reduce heat losses. Previous studies have indicated that internal macro-scale convection cells can occur in thick porous insulation layers, decreasing the thermal performance of the envelope component. The focus of previous studies has been on horizontal insulation layers, most often composed of glass wool. Therefore, there is a lack of empirical data for loose-fill insulation and, in particular, bio-based materials, which have the potential of being more sustainable than conventional ones. The present investigation of this paper looks at the possibility of internal macro-scale convection inside loose-fill wood fiber insulation in a full-scale vertical wall element, with the modified Rayleigh number in the current investigation being between 20 and 45 and exhibiting internal convection in all cases. The experimental results show good agreement in terms of heat flux and temperature distribution with numerical simulations where the macro-scale convection is modelled explicitly. It also indicates that internal macro-scale convection can be modelled with existing building physics simulation tools, such as COMSOL. Finally, the internal macro-scale convection increases the effective U-value by up to 90 % for the highest temperature difference in steady-state conditions. This effect appears to diminish under dynamic boundary conditions, with a calculated effective U-value being within the uncertainty of the steady-state case with the lowest temperature difference, indicating that it might be less influential under real conditions.</p

Adaptive Ensemble Control for Stochastic Systems With Mixed Asymmetric Laplace Noises

This article presents an adaptive ensemble control for stochastic systems subject to asymmetric noises and outliers. Asymmetric noises skew system observations, and outliers with large amplitude deteriorate the observations even further. Such disturbances induce poor system estimation and degraded stochastic system control. In this work, we model the asymmetric noises and outliers by mixed asymmetric Laplace distributions (ALDs) and propose an optimal control for stochastic systems with mixed ALD noises. Particularly, we segregate the system disturbed by mixed ALD noises into subsystems, each of which is subject to a specific ALD noise. For each subsystem, we design an iterative quantile filter (IQF) to estimate the system parameters using system observations. With the estimated parameters by the IQF, we derive the certainty equivalence (CE) control law for each subsystem. Then we use the Bayesian approach to ensemble the subsystem CE controllers, with each of the controllers weighted by its posterior probability. We finalize our control law as the weighted sum of the control signals by the subsystem CE controllers. To demonstrate our approach, we conduct three numerical simulations and Monte Carlo analyses. The results show improved tracking performance by our approach for skew noises and its robustness to outliers, compared with the RLS-based control policy.</p

Compressing High-Frequency Time Series Through Multiple Models and Stealing from Residuals

Wind turbines are equipped with high-quality sensors that generate vast volumes of high-frequency time series. The time series are ingested on the edge and transferred to the cloud for later analytics. This process is complicated by challenges like low network bandwidth and high cloud storage costs. ModelarDB was proposed as a solution to efficiently manage time series across the entire pipeline by using so-called models for lossless or error-bounded lossy compression of time series. However, ModelarDB’s compression can be further improved through: 1) avoiding models that only represent few values by storing residuals (i.e., values that models fail to compress) explicitly with them; 2) exploiting error bounds even more through preprocessing; and 3) timestamp compression specialized for regular and irregular time series. We propose the multi-model compression method Fauna which uses 1) the novel model fitting method Platypus; 2) PMC and Swing for compressing values and; 3) the novel Macaque for compressing residuals and timestamps. Platypus is a model fitting method that uses different models for specialized compression of values and residuals. We then evaluate state-of-the-art lossless compression methods for 32-bit floats and propose preprocessing methods to add support for error-bounded compression. We present Macaque that includes MacaqueV and MacaqueTS. MacaqueV modifies Facebook Gorilla’s lossless compression method for 32-bit floats (GorillaV) and combines it with our novel preprocessing methods to now also enable error-bounded lossy compression. MacaqueTS is a lossless compression method for timestamps. Using only Platypus reduces ModelarDB’s storage use by up to 1.8x and significantly simplifies using the system. While also up to 7x better for lossless compression, ModelarDB with Fauna uses up to 2.5x less storage than ModelarDB and up to 14.5x, 7.2x, 17.5x and 14.2x less storage than ClickHouse, Apache IoTDB, Apache Parquet and TimescaleDB, respectively, with a realistic 1% error bound

‘Simplification’ and ‘accessibility’ in diaphasic intralingual translation:A semantic interpretation

As translational phenomena, ‘simplification’ and ‘enhanced accessibility’ are phenomena that are especially associated with diaphasic, or interregisterial, intralingual translation of the expert-to-lay variety, i.e., adaptation of specialized LSP source texts for a lay target audience (henceforth Diaph-intra). Previous research into simplification and enhancement of accessibility in Diaph-intra has mostly focused on shifts at the levels of lexicogrammar (Hill-Madsen 2015a, 2015b, 2019, 2022, Muñoz-Miquel 2012, Ezpeleta Piorno 2012) and context (Hill-Madsen 2024), whereas the level of semantics has largely been bypassed. To remedy this shortcoming, this chapter offers a semantic interpretation of ‘simplification’ and ‘enhanced accessibility’ as intralingual translation strategies. Three semantic parameters are relevant to lay-oriented Diaph-intra, viz. epistemic-semantic density (concerned with the complexity of epistemic/denotational content in wordings), semantic gravity (concerned with degrees of concreteness/abstractness) and interpersonal engagement (presence/absence of interpersonal meanings). Using source-target pairs from the field of medicine, the article illustrates shifts within all three semantic dimensions