Publikationer från KTH
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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
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
Sustained impact of higher customer satisfaction on bank revenue
No full textThis study examines the relationship between customer satisfaction and individual-level bank revenue growth, drawing on data from 19,060 Swedish retail banking customers that combine survey responses with objective bank records. Furthermore, we investigate whether the impact of satisfaction on revenue growth is sustained over time, specifically one, two three and four years after the measurement of satisfaction, and whether this effect differs across customer satisfaction levels. The results show that higher satisfaction is associated with greater sustained revenue growth, with more pronounced effects for customers in the medium-high and highest satisfaction groups. By contrast, no significant sustained revenue growth is found for customers with low and low-medium satisfaction. The findings do not support the hypothesis of diminishing returns when moving from medium-high to the highest satisfaction levels, although weak indications suggest scope for further exploration. Overall, the findings demonstrate the long-term revenue growth of satisfied customers and emphasize the importance of targeting customers with lower to medium-low satisfaction to enhance overall revenue performance.QC 20251202</p
Carbon trapping at the solid–liquid interface in cemented carbides
No full textInter-diffusion between the hard ceramic and ductile metallic phases in composite materials such as cemented carbides governs their mechanical properties. Understanding atomic-scale diffusion at these interfaces is key to uncovering the mechanisms that dictate microstructure evolution, establishing a foundation for tailoring the properties of WC Co composites through precise interfacial control. The interface between tungsten carbide (WC) and liquid cobalt (Co) is investigated using molecular dynamics simulations. An integrated approach is presented for computing the solid–liquid interfacial free energy by combining computer vision aided interface detection with the Capillary fluctuation method. Significant inter-diffusion is observed, with atomic displacements primarily localized at the interface for W and C, while Co exhibits homogeneous behavior in the liquid phase. The formation of C C bonded structures at the interface is identified as a critical factor influencing diffusion, introducing localized structural rigidity that reduces atomic mobility. Additionally, premelting phenomena below bulk melting temperatures gives rise to a heterogeneous interfacial zone containing residual solid WC patches and molten W-Co alloy. The inter-diffusion coefficients for W, C, and Co compare well with prior computational and experimental studies, validating the methodology. These findings offer new insights into the atomic-scale mechanisms driving interface evolution and provide a foundation for tailoring the properties of WC Co composites through precise interfacial control.QC 20251215</p
Applying a conflict typology to ecologies of intermediation: the case of a transitions intermediary in Spain
No full textWithin ecologies of intermediation, multiple intermediaries and their initiatives operate with overlapping remits and associated conflicts. To understand these, we propose a typology of conflict sources, applying this to the case of a university-based sustainability intermediary in Spain (itdUPM). itdUPM acts as an umbrella organisation for initiatives that connect stakeholders in pursuit of sustainability objectives. Analysing the case with its embedded sub-units, we distinguish between: (1) value conflicts connected to actors’ identities and ideologies; (2) socio-cognitive conflicts related to the need to hold consistent and socially validated cognitions; (3) conflicts of interests, which emerge when actors’ interests in terms of resource allocation, including power, are misaligned. We highlight the importance of a nuanced understanding of conflict types as part of intermediation processes, including those arising from the dynamic interactions between specific intermediation initiatives and their broader contexts. We also propose conflict management strategies to assist in this in practice.QC 20251124</p
Circular economy in the extractive frontier: Tensions and pathways for transformative change in mining
No full textThe mining sector, like other sectors of the economy, is under increasing pressure to adopt circular economy (CE) principles across its value chains and core operations. This paper offers a critical and conceptually grounded contribution to understanding how CE can support systemic transformation in one of the most resource-intensive and path-dependent sectors of the global economy. It examines the structural and institutional conditions that shape the adoption of CE in mining and identifies key tensions that constrain or enable transformative change. In parallel, the paper explores emerging pathways informed by technological innovation, shifts in production routines, and the rise of new circular business models. These insights are synthesised into a multi-level framework that captures the dynamic interactions between micro-, meso-, and macro-level processes shaping CE transitions. In addition to offering a diagnostic perspective, the framework outlines concrete action points for advancing systemic change.QC 20250908</p
Modeling and Simulating Cyberattacks with Dynamic Graphs : With applications to cloud security assessments
No full textThis dissertation presents a formalism for exploring two fundamental, yet underrepresented, cyberattack dynamics. Namely, how adversary actions drive the emergence of cyberattacks and how adversaries manipulate dynamic system structures, such as by creating and destroying objects. The formalism in question is encapsulated in the Dynamic Meta Attack Language (DynaMAL), a meta-level formalism for modeling and simulating cyberattacks with dynamic graphs. DynaMAL has been designed and developed in accordance with the design science research framework across four studies. The first study introduces an attack graph construction language for assessing cloud architectures and identifies the central problem of representing attacks in which adversaries manipulate dynamic system structures. The second study is a systematic literature review of cyberattack simulations that identifies key simulation concepts used in later stages of the design process. Building on the two initial studies, the third study establishes the cyberattack modeling foundations of DynaMAL, comprising a dynamic graph system, a multi-layered graph model, a lazy graph generation strategy, and the DynaMAL grammar. Finally, the fourth study develops the corresponding discrete-event simulation process for DynaMAL. The resulting capabilities are evaluated through a first simulation experiment that uses three cloud penetration testing scenarios that rely on dynamically creating and destroying resources. The scenarios are then solved automatically with near-optimal results by combining two search and optimization algorithms.I den här avhandlingen presenteras en formalism för att utforska två fundamentala men underrepresenterade cyberattackdynamiker. Dessa är hur antagonisters handlingar driver fram cyberattacker och hur antagonister manipulerar dynamiska systemstrukturer, till exempel genom att skapa och förstöra resurser. Formalismen i fråga är inkapslad i ett Dynamic Meta Attack Language (DynaMAL), en formalism på metanivå för att modellera och simulera cyberattacker med dynamiska grafer. DynaMAL:s design och utveckling fortlöper genom fyra studier utförda i enlighet med designforskningsramverket. Den första studien bidrar med ett attackgrafkonstruktionsspråk för att utvärdera molnarkitekturer, vilket utvecklar problematiken med att representera när antagonister manipulerar dynamiska systemstrukturer. Den andra studien är en systematisk litteraturstudie som granskar cyberattacksimuleringsforskning och uppdagar flertalet nyckelkoncept som understödjer de senare designaktiviterna. I den påföljande tredje studien etableras ett fundament för cyberattackmodellering innefattandes ett dynamiskt grafsystem, en lagerbaserad grafmodell, en lat grafgenereringsstrategi och DynaMAL-grammatiken. Den fjärde studien färdigställer DynaMAL-formalismen genom att implementera en motsvarande diskret händelsestyrd simuleringsprocess. De resulterande förmågorna utvärderas via ett första simuleringsexperiment, varvid tre molnpenetrationstestningsscenarion som krävde att resurser dynamiskt skapades eller förstördes används. Scenariona löses sedan automatiskt med nära inpå optimala resultat genom att kombinera två sök- och optimeringsalgoritmer.QC 20251219</p
The role of pressure and steam on pyrolysis of biogenic waste : Value-added commodity products
No full textThe global concerns regarding climate change and the steep increase in greenhouse gas emissions, driving the society to transform biogenic waste into renewable value-added products. Thermal depolymerization of biomass is regarded as the most promising thermochemical conversion technology to produce bio-oil, biochar and syngas. To date, previously published review articles revolve around various biomass pyrolysis aspects, such as the chemistry of biomass, application of pyrolysis products, effects of pyrolysis parameters and kinetics, effect of various catalysts on product yield, upgradation strategies, and process technologies. The commercialization of conventional pyrolysis technology is challenging due to the inferior oil properties (oxygenates nearly 40 wt%), agglomeration, feeding constraints, ash content in the biomass, heat and mass transfer limitations, pressure build-up due to tar formation and thermal distribution across the reactor. Pressurized steam pyrolysis of biomass improves the product quality (oil, char and gas) with a production of value-added chemicals. Despite this, studies regarding the combined effect of steam and pressure on product quality from pyrolysis technology are not available in the literature. This study offers a comprehensive overview of the state-of-the-art on the effect of pressure and steam on biogenic pyrolysis. Additionally, the study also explains how pressure and steam can be utilized to improve the properties of the pyrolysis products. The review examine the fundamentals of biomass conversion, the effect of pressure and steam, with interlaid mechanisms on biomass conversion and challenges with pressurized steam pyrolysis. Finally, the benefits of products in various applications and a conceptual process perspective of pressurized steam pyrolysis are briefly outlined.QC 20251028</p
Developing and validating domain specific languages for cyberattack modeling and simulations
No full textThis thesis explores the potential of domain-specific languages (DSLs) to enhance the accuracy, efficiency, and expressiveness of cyberattack modeling and simulation. Motivated by the increasing sophistication of cyber threats, this work addresses the limitations of traditional modeling approaches by developing and validating two novel DSLs: one tailored for vehicular systems and another for the Information and Communications Technology (ICT) domain. These languages provide specialized vocabulary and syntax for describing attack patterns, system behaviors, and defense mechanisms concisely and straightforwardly. Through a series of experiments and case studies, this research demonstrates the effectiveness of these DSLs in capturing the complexities of real-world cyberattacks. These languages enable the automatic generation of attack graphs from system architecture models, streamlining threat identification and enhancing the alignment of security measures with established frameworks for cybersecurity professionals. This thesis contributes to the advancement of cyberattack modeling and simulation techniques, providing cybersecurity professionals with tools to express, analyze, and predict the behavior of cyberattacks.Denna avhandling undersöker potentialen hos domänspecifika språk (DSL) för att förbättra noggrannheten, effektiviteten och uttrycksfullheten i modellering och simulering av cyberattacker. Motiverad av den ökande sofistikeringen av cyberhot, adresserar detta arbete begränsningarna hos traditionella modelleringsmetoder genom att utveckla och validera två nya DSL:er: en skräddarsydd för fordonsystem och en annan för IKT-domänen. Dessa språk tillhandahåller specialiserad vokabulär och syntax för att beskriva attackmönster, systembeteenden och försvarsmekanismer på ett koncist och tydligt sätt. Genom en serie experiment och fallstudier visar denna forskning effektiviteten hos dessa DSL:er för att fånga komplexiteten i verkliga cyberattacker. Dessa språk möjliggör automatisk generering av attackgrafer från systemarkitekturmodeller, vilket effektiviserar hotidentifiering och förbättrar anpassningen av säkerhetsåtgärder till etablerade ramverk för cybersäkerhets-experter. Denna avhandling bidrar till utvecklingen av tekniker för modellering och simulering av cyberattacker, vilket ger cybersäkerhetsexperter verktyg för att uttrycka, analysera och förutsäga beteendet hos cyberattacker.QC 20251217</p
Learning 3D Texture-Aware Representations for Parsing Diverse Human Clothing and Body Parts
No full textExisting methods for human parsing into body parts and clothing often use fixed mask categories with broad labels that obscure fine-grained clothing types. Recent open-vocabulary segmentation approaches leverage pretrained text-to-image (T2I) diffusion model features for strong zero-shot transfer, but typically group entire humans into a single person category, failing to distinguish diverse clothing or detailed body parts. To address this, we propose Spectrum, a unified network for part-level pixel parsing (body parts and clothing) and instance-level grouping. While diffusion-based open-vocabulary models generalize well across tasks, their internal representations are not specialized for detailed human parsing. We observe that, unlike diffusion models with broad representations, image-driven 3D texture generators maintain faithful correspondence to input images, enabling stronger representations for parsing diverse clothing and body parts. Spectrum introduces a novel repurposing of an Image-to-Texture (I2Tx) diffusion model—obtained by fine-tuning a T2I model on 3D human texture maps—for improved alignment with body parts and clothing. From an input image, we extract human-part internal features via the I2Tx diffusion model and generate semantically valid masks aligned to diverse clothing categories through prompt-guided grounding. Once trained, Spectrum produces semantic segmentation maps for every visible body part and clothing category, ignoring standalone garments or irrelevant objects, for any number of humans in the scene. We conduct extensive cross-dataset experiments—separately assessing body parts, clothing parts, unseen clothing categories, and full-body masks—and demonstrate that Spectrum consistently outperforms baseline methods in prompt-based segmentation.QC 20251219</p