Digitala Vetenskapliga Arkivet - Academic Archive On-line
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Estimating Street-Level Green View Index Using Satellite Remote Sensing and Explainable Machine Learning
No full textStreet-level greenery assessment through the Green View Index (GVI) is essential for transportation planning and sustainable urban development. However, current GVI estimation methods rely heavily on street-view imagery, facing significant scalability challenges including high data acquisition costs, inconsistent temporal coverage, and limited cross-regional comparability. This study proposes an explainable machine learning framework for GVI estimation using ubiquitous satellite remote sensing data from Sentinel-2A imagery. Eight complementary spectral indices were systematically evaluated: enhanced vegetation indices, spectral variation indices, and urban context indices. Multiple spatial buffer configurations (200m to 1000m) and input strategies were tested, including mean-value aggregation and raster input for CNN models. The methodology was validated using over 6,300 samples from Helsinki (Finland), with comprehensive comparisons across traditional machine learning models and deep learning architectures.The optimized XGBoost model with 1000m buffer achieved R2 = 0.67 and MSE = 0.022, improved approximately 20% over baseline approaches using only NDVI and RGB bands. SHAP (SHapley Additive exPlanations) analysis revealed spatial scale-dependent feature importance patterns, with Urban Index (UI) consistently ranking highest across all scales, while NDVI showed lower importance at larger buffer sizes. Alternative spatial modeling approaches (CNN) underperformed compared to mean-value aggregation methods, suggesting that statistical aggregation may be more robust for GVI predictions. The framework eliminates dependency on street-view imagery while maintaining competitive accuracy, offering a scalable solution for large-scale green infrastructure assessment. The explainable AI approach provides interpretable insights into environmental factors influencing street-level green perception, supporting evidence-based decision-making in transportation planning and active transportation network development
Development and evaluation of AI chatbot tool for written communication training in self-care : Experiences of pharmacy students and faculty
No full textBackground: Effective communication is crucial in pharmacy practice, particularly in self-care counseling. As online pharmacies and chat-based consultations expand, training in digital written communication is increasingly important. Artificial intelligence (AI) systems based on large language models (LLMs) offer a structured and engaging environment to support skill development through conversational agents. This study explored the use of LLM-based chatbots to train pharmacy students in written synchronous communication for self-care consultations. Methods: Three chatbot-simulated patients and an LLM-based feedback system were developed to reflect common self-care scenarios and provide communication-focused feedback. Fourteen pharmacy students and faculty interacted with the chatbots and shared their experiences through semi-structured interviews. Thematic analysis was used to identify patterns in the data. Results: The analysis identified five main themes. Participants emphasized the authenticity of the simulated patient interactions, particularly their emotional realism. The AI-generated feedback was described as structured, detailed, and fair especially valued for its focus on communication skills. Faculty appreciated the consistency of the feedback and highlighted its added value to complement human assessment. Students discussed the cognitive and emotional demands of the experience, suggesting potential to tailor chatbot complexity to learners' needs. Conclusion: LLM-based chatbots represent a pedagogically grounded and scalable tool for developing pharmacy students' written communication skills in self-care consultations. This approach offers a foundation for building shared virtual patient infrastructures and integrating communication theory into digital education. It holds promise for broad implementation across pharmacy programs adapting to the demands of online and hybrid care
Christoffel transform and multiple orthogonal polynomials
No full textWe investigate multiple orthogonal polynomials associated with the system of measures obtained by applying a Christoffel transform to each of the orthogonality measures. We present an algorithm for computing the transformed recurrence coefficients and determinantal formulas for the transformed multiple orthogonal polynomials of type I and type II. We apply these results to show that zeros of multiple orthogonal polynomials of an Angelesco or an AT system interlace with the zeros of the polynomials corresponding to its onestep Christoffel transform. This allows us to prove a number of interlacing properties satisfied by the multiple orthogonality analogues of classical orthogonal polynomials. For the discrete polynomials, this also produces an estimate on the smallest distance between consecutive zeros. We also identify a connection between the Christoffel transform of orthogonal polynomials and multiple orthogonality systems containing a finitely supported measure. In consequence, the compatibility relations for the nearest neighbour recurrence coefficients provide a new algorithm for the computation of the Jacobi coefficients of the one-step or multi-step Christoffel transforms
Advancing an already high-performance smart building with model predictive control: Multi-layer optimization under forecast uncertainty in a real building case
No full textThermal energy systems in buildings play a central role in global decarbonization efforts, accounting for a significant share of energy use and carbon emissions. This study addresses a key research question: how can advanced control strategies further enhance the performance of already energy-efficient, low-exergy thermal systems in low-energy buildings? To address this, a model predictive control (MPC) framework is designed to optimize the operation of an advanced thermal system based on modern concepts of low-temperature heating and high-temperature cooling, including ground-source heat pumps, borehole thermal storage, and modern air handling units. This approach employs a multi-layered MPC cost function, considering both immediate operational costs (electricity and heating) as well as system impact penalties, such as CO₂ emissions, thermal energy storage preservation, comfort violations, and peak load shaving, in response to fluctuating market cost signals, outdoor temperature, and thermal storage limitations. Applied to a validated, ultra-efficient commercial building, the MPC framework achieves a 13 % reduction in annual market-responsive operational costs, a 20 % improvement in long-term savings, and a four-year shorter payback period compared to existing well-established rule-based control. The results further confirm the robustness of predictive control under realistic forecast errors, as demonstrated by Monte Carlo simulations. From an environmental perspective, the CO₂ emission index stays below both Swedish electricity and district heating baselines, demonstrating the environmental benefits of predictive control through strategic sector coupling. Beyond the case study, the proposed method provides a scalable pathway for integrating predictive control into next-generation smart buildings. It highlights the potential of MPC as the final optimization layer in advanced thermal systems, aligning with global objectives for cost-promising and carbon-neutral building operations. QC 20250806</p
Next-Day Effects of Social Drinking on Driver Fatigue and Driving Performance
No full textBinge drinking of alcohol leads to worsened driving performance the following morning, even when the blood alcohol concentration (BAC) has returned to or is close to zero. The objective of this study was to investigate the effects of social drinking (BAC = 0.05%) on next-day driver fatigue and driving performance. A homogenous sample of 32 experienced male drivers drove for 35 min on rural and urban roads in a driving simulator, both the day after drinking alcohol and the day after a sober evening. The main effects on next-day performance were ambiguous, where self-assessments showed lower next-day performance and higher subjective sleepiness after drinking alcohol in the evening, whereas variability in lateral position, heart rate variability and attention showed worse next-day performance in the control condition. Overall, the effect sizes were small. The results indicate that a BAC level of 0.05% does not influence next-day performance after a full night’s sleep to any greater extent. Further studies including a placebo condition are needed to verify this result, also considering more BAC levels, long-term effects of habitual drinking, and longer driving times.PANACE
AI Data Governance : Overlaps Between the AI Act and the GDPR
No full textThis article examines the overlaps between the AI Act and the GDPR, analysing their overall relationship, conceptual similarities and differences, as well as specific provisions in the AI Act that explicitly overlap with the GDPR. The primary focus of this article lies on AI data governance, with a detailed analysis of the requirements set out in Article 10 AI Act. This provision establishes quality criteria for data and data governance in high-risk AI systems that rely on training AI models with data. The article introduces a novel approach to understanding, interpreting, and applying these criteria to facilitate GDPR compliance. As a result, we propose a principles-based framework for AI data governance, categorising the quality criteria in Article 10 into three overarching principles: data accuracy, data transparency, and data fairness. To ensure practical quality assurance, providers of high-risk AI systems should adopt specific methods outlined in the AI Act, such as data-preparation processing operations and the processing of personal data for bias detection and correction. Finally, we propose a cycle-approach to AI data governance, aligning the requirements of Article 10 AI Act with the limitations imposed by the GDPR
Effects of Rear Lights on Cars by Daylight
No full textEffects of rear-light usage by daylight were examined. A field experiment measured passing vehicles’ lateral distance to a stationary car, as an effect of rear-light status. A questionnaire survey measured car drivers’ perceptions regarding safety and visibility depending on rear-light usage, and their experiences, preferences, and beliefs about own usage of rear lights in daylight. An observational study measured percentages of cars with lit and unlit rear lights, respectively, in sunshine during summer. Results show that rear lights give greater lateral distance when being passed by. A car with lit rear lights is interpreted as more likely to start driving. Lit rear lights are preferred and estimated to give better visibility and traffic safety. Only a small proportion of drivers know that they drive with unlit rear lights in daylight, whilst the majority actually drive with unlit rear lights. Conclusion: lit rear lights in daylight improves traffic safety. It is therefore recommended that rear lights with sufficient brightness to be seen even in sunshine should be legally required
Empowering women through radio : Evidence from Occupied Japan
No full textI study the impact of women's radio programs that the US-led occupying force aired nationwide in Occupied Japan (1945-1952) to dismantle the prewar patriarchal norms. From the perspective of the economics of identity, the radio messages can be viewed as attempts to alter gendered identity norms, and thus to shift women's political, economic and family outcomes. Using local variation in radio signal strength driven by soil conditions as an instrumental variable, I show that greater exposure to women's radio programs increased women's electoral turnout, and the vote share for female candidates, highlighting women's votes matter. I find no effects on women's labor market outcomes, but exposure to women's radio programs accelerated the postwar fertility transition. Overall, disseminating pro-gender-equality messages can have significant implications for both women's lives and society at large, potentially paving the way for rapid economic growth that would follow
Promoting construction innovation : A public infrastructure client’s adaptation of procurement and project management strategies
No full textThe construction and infrastructure sector is a significant contributor to global carbon emissions, necessitating substantial changes to support, what can be called, the sustainability transition. Public infrastructure clients are expected to lead this transition by promoting construction innovation, while engineering consultants, involved in the planning and design of projects, play a key role in supporting these efforts. Public procurement is widely recognized – politically – as a key strategic tool for promoting innovation and advancing sustainability, despite the project-based sector the role of project management remains largely overlooked. However, previous research highlights the importance of both procurement and project management strategies in promoting construction innovation. To effectively promote innovation, these strategies must be adapted to the specific characteristics of each project and should emphasize flexibility and involvement of actors. Despite the procurement and project management strategies’ acknowledged significance, they are often treated as separate governance mechanisms within previous research, failing to account for their interconnected nature. The purpose of this thesis is to increase the understanding of how a public infrastructure client promotes innovation towards the sustainability transition through adaptation of procurement and project management strategies in planning and design of new infrastructure. A longitudinal single-case study of the largest public infrastructure client in Sweden, the Swedish Transport Administration (Trafikverket), provides empirical insights. The findings show that despite high expectations for public clients to promote innovation – especially in designated pilot projects – these projects are evaluated and managed through conventional linear processes, short-term goals, and paradigms. While innovation is acknowledged as a multidimensional concept requiring flexibility and collaboration, procurement and project management strategies remain predominantly control-oriented, emphasizing efficiency, problem-solving, and monitoring. This approach is found to limit the perceived promotion and impact of construction innovation in practice. By problematizing the concept of comprehensive governance – integrating procurement and project management – this thesis highlights how public clients’ reliance on detailed process control and the aligning with a hard paradigm, which is not well-suited to promote construction innovation. The research underscores the interdependence of procurement and project management strategies, advocating for a holistic governance perspective in construction management research and practice. Addressing this interconnection, both within research and practice, is crucial for developing strategies that effectively promotes construction innovation supporting the sustainability transition in public infrastructure projects
Experimental Investigations of Ecohydraulic Flows in Shallow Waterways with Large Bed Roughness
No full textHydropower's regulatory capacity is crucial in balancing the increasing integration of intermittent renewable energy sources, such as wind and solar power, into the energy system. This is anticipated to result in frequent start-stop cycles in hydropower operations, leading to increased flow fluctuations, commonly known as hydropeaking, which may negatively impact the riverine ecosystem. According to the European Union’s Water Framework Directive, all water bodies in the EU should achieve good ecological status. Therefore, it is essential to predict the ecological effects of hydropower using hydraulic modeling tools. In rapids, tributaries, and small streams, where the bed structure is in comparable scale to the water depth, the flow fields become highly disturbed. Such variations in bathymetry can play important ecological roles. Regions characterized by large bed roughness and fluctuating discharge can support multiple aquatic species simultaneously, forming so-called complex habitats. These conditions render empirical assumptions of flow resistance, such as the Gauckler-Manning coefficient, inadequate for accurately describing hydraulic behavior in these streams, leading to inaccurate predictions of water depth and local flow properties. To improve flow resistance formulation in shallow areas with large bed roughness, a broader understanding of the flow field response to changes in flow depth is required. This study investigates the effect of relative submergence (i.e., water depth relative to boulder size) on flow field characteristics through flume experiments. Idealized stone shapes (hemisphere and cube) are used to resemble varying river conditions. A Lagrangian Particle Tracking (LPT) method, commonly known as 3D Particle Tracking Velocimetry (PTV), is employed to capture time-resolved volumetric data with high spatial resolution, enabling measurements close to objects. The results provide insights into fundamental hydrodynamics and support ecohydraulic measures, such as river restoration efforts and solutions for fish passage. Additionally, qualitative discussions explore the potential effects on fish habitats due to variations in submergence and additional roughness elements. These experiments also serve as reference cases for validating Computational Fluid Dynamics (CFD) models. The findings of this work serve as a foundation for future research involving larger, more natural boulders and lower relative submergences in wider flumes, improving our understanding of ecohydraulic processes at larger spatial scales