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High-order discretized ACMS method for the simulation of finite-size two-dimensional photonic crystals
No full textThe computational complexity and efficiency of the approximate mode component synthesis (ACMS) method is investigated for the two-dimensional heterogeneous Helmholtz equations, aiming at the simulation of large but finite-size photonic crystals. The ACMS method is a Galerkin method that relies on a non-overlapping domain decomposition and special basis functions defined based on the domain decomposition. While, in previous works, the ACMS method was realized using first-order finite elements, we use an underlying hp–finite element method. We study the accuracy of the ACMS method for different wavenumbers, domain decompositions, and discretization parameters. Moreover, the computational complexity of the method is investigated theoretically and compared with computing times for an implementation based on the open source software package NGSolve. The numerical results indicate that, for relevant wavenumber regimes, the size of the resulting linear systems for the ACMS method remains moderate, such that sparse direct solvers are a reasonable choice. Moreover, the ACMS method exhibits only a weak dependence on the selected domain decomposition, allowing for greater flexibility in its choice. Additionally, the numerical results show that the error of the ACMS method achieves the predicted convergence rate for increasing wavenumbers. Finally, to display the versatility of the implementation, the results of simulations of large but finite-size photonic crystals with defects are presented
Praeteritum transcriptum. A Transkribus Tribute: Celebrating our First Five Years as a Cooperative (2019-2024)
No full textPraeteritum transcriptum: A Transkribus Tribute This publication commemorates READ-COOP SCE's first five years (2019-2024), examining how the cooperative structure behind Transkribus has transformed digital humanities and historical scholarship. Beyond celebrating technological achievements in automated text recognition (HTR/OCR), the work analyzes how a stakeholder-owned cooperative model offers a sustainable alternative to commercial platforms for cultural heritage preservation. The tribute documents Transkribus' ecosystem as co-created by diverse stakeholders—software engineers, archivists, historians, and librarians—who serve as contributors, owners, and decision-makers rather than mere users. Through international case studies spanning various languages, scripts, and disciplines, it demonstrates the platform's adaptability to diverse scholarly requirements. Central to the analysis is the productive symbiosis between tool developers and humanities scholars, where pooled user data has enabled the development of comprehensive historical text recognition models. The publication positions Transkribus as not merely a technological solution but as an ethical framework for AI development grounded in collective governance, transparency, and scholarly collaboration, ultimately redefining approaches to our shared written heritage
Response to the letter to the editor – Brain Pro-TCT: a prospective, quasi-experimental study on early delirium detection with Delirium Observation Screening Scale versus single-channel EEG after cardiac surgery in patients aged over 70 years
No full textWe thank Dr. Koizia and colleagues for their interest and careful review of our article. We appreciate the opportunity to respond to Dr. Koizia et al. regarding our Brain Pro-TCT study, in which we compared the nurse-reported Delirium Observation Screening Scale with single-channel EEG (SC-EEG) after cardiac surgery
Good governance of public sector AI:A combined value framework for good order and a good society
No full textGood governance of AI-supported public services means that they should function in a democratic and rule-of-law manner (“good order”) and consider the just treatment and wellbeing of citizens (“good society”). To gain insight into relevant “good order” and “good society” values, this study uses AI ethics and public administration literature to develop a comprehensive value framework for the good governance of public sector AI. We identify values pivotal to the AI-public sector nexus through a dual-phase analysis. First, we identify seven core values: five “good order” core values (responsiveness, effectiveness, procedural justice, resilience, and counterbalance) and two “good society” core values (wellbeing, social justice). Subsequently, delving into 33 studies spanning AI ethics and public administration, we identify operational values related to the core values. The operational values provide further interpretation of the core values and operationalize them. This second round in our research also shows that the seven core values found during the first round indeed account for value considerations encountered by scholars so far. In this way, we arrive at a robust value framework for the good governance of AI use in the public sector. The framework is not a one-size-fits-all recipe for public sector AI but a guide for policymakers to consider both democratic and ethical values. It can address gaps in both research fields, analyze moral dilemmas in AI policy tools like public-private partnerships, and aid policymakers in blending abstract values with contextual decision-making
Functional modes and social well-being as a protector of mental health during COVID-19
No full textObjective: This study investigates how well-being and adaptive coping post-inpatient schema-focused therapy (SFT) predict mental health during COVID-19 in individuals with Personality Disorders (PD), 2-8 years after treatment. Method: Using a naturalistic, prospective within-subject design, 52 PD-diagnosed participants completed assessments post-treatment and at long-term follow-up, with 20 pre-COVID and 32 during COVID. Measures included Schema Mode Inventory, Mental Health Continuum Short Form, and Brief Symptom Inventory. Correlations and hierarchical multivariate regression were used. Results: COVID-19 was associated with lower social and psychological well-being. Social well-being at treatment completion and functional modes predicted changes. Conclusion: Inpatient SFT enhances long-term resilience by improving social well-being and functional modes, benefitting well-being during COVID-19. Clinical significance: COVID-19 has significantly impaired mental health, particularly for individuals with complex PD. This article seeks insight into factors that contribute to resilience in individuals with PD who underwent treatment 2–8 years prior, especially during stressful periods, like COVID-19.</p
Identifying landscape patterns at different scales as driving factors for urban flooding
No full textClimate change and rapid urbanization have led to increasingly frequent urban flooding, causing substantial losses. While previous studies have examined the impact of land use types on flooding, few studies have explored how the spatial distribution and configuration of land use (landscape patterns) influence urban flooding across different scales. This study addresses this gap by investigating the effects of landscape patterns on urban flood events in Chengdu, China. We constructed a comprehensive dataset comprising 28 flood influencing factors, including landscape pattern, topographic, and hydrological characteristics. Using Principal Component Analysis (PCA), we classified these variables and applied stepwise Poisson regression to evaluate how landscape patterns affect urban flooding. Our findings show that key influencing factors vary by scales: at the 1 km scale, topographic factors were most important; at the 2 km scale, impervious areas had the largest impact; and at the 3 km scale, landscape configuration factors were dominant. In particular, the mean patch area and cohesion were consistently significant across all scales, indicating that more fragmented and dispersed landscapes tend to reduce flooding occurrence. We conclude that scale is an important determinant for properly understanding the contribution of landscape patterns to urban flood mitigation.</p
Exploring How Facilitators Influence Teacher Talk During Post-Research Lesson Discussions of Lesson Study
No full textWhen teachers participate in lesson study (LS), their learning depends greatly on inquiry-oriented talk, in which they interact to understand and challenge one another’s perspectives. Previous research has shown that an LS facilitator can add value to this type of talk. In a two-case study, this research investigates how facilitators influence teacher talk during two post-research lesson discussions (PLDs). The findings show that how the facilitator communicates the PLD’s rationale, organizes conversational phases, and supports framing the PLD’s goals and questions can influence teacher talk. This study provides in-depth insights into the challenges of facilitating PLDs and identifies ways to support facilitators in developing and adjusting existing support and training.</p
Biofuel production using tire waste in fast pyrolysis:A life cycle assessment study
No full textDespite exceeding 2 billion units annually, the management of waste tires remains a global challenge. Pyrolysis, a thermochemical conversion process, offers a potential waste-to-energy solution with valuable byproducts. However, concerns regarding its environmental sustainability, particularly emissions associated with sulfur content, persist. The aim of this study is to evaluate the environmental impacts of a large-scale pyrolysis reactor processing waste tires using life cycle assessment (LCA) as an environmental impact tool. In this study, using heating fuel oil (HFO) and non-condensable gas (NCG) as heating methods, two pyrolysis scenarios were investigated in terms of their environmental performance. The LCA identified abiotic depletion and acidification as key environmental concerns. The HFO scenario has shown a 15% greater impact on abiotic depletion and a 78% greater impact on acidification compared to the NCG scenario. This difference has been attributed to the higher sulfur content in HFO. Nevertheless, both pyrolysis scenarios have shown positive environmental benefits regarding marine and freshwater ecotoxicity compared to conventional alternatives. These findings support the potential of pyrolysis for sustainable waste tire management while highlighting the need for further optimization to minimize sulfur-related emissions. The findings of this study can be used as a roadmap towards adapting clean technologies such as pyrolysis as a waste management strategy for policymakers by applying holistic system boundaries and regulated pyrolysis production.</p
Bayesian Modeling of Longitudinal Multiple-Group IRT Data with Skewed Latent Distributions and Growth Curves
No full textIn this work, we introduce a multiple-group longitudinal IRT model that accounts for skewed latent trait distributions. Our approach extends the model proposed by Santos et al. in 2022, which introduced a general class of longitudinal IRT models. The latent traits follow a multivariate skew-normal distribution, induced by an antedependence structure with centered skew-normal errors. Additionally, latent mean trajectories are modeled using quadratic curves, while structured covariance matrices capture within-participant dependencies. A three-parameter probit model is employed for dichotomous items. Bayesian parameter estimation and model fit assessment are conducted through a hybrid MCMC algorithm, combining the FFBS sampler with Metropolis-Hastings steps. The model’s effectiveness is demonstrated through an application to real data from the Longitudinal Study of the 2005 School Generation in Brazil (GERES project), where it outperforms the normal model by better capturing asymmetry in latent traits. A simulation study further supports its robustness across various test conditions.</p