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Evaluating the impact of district-level microclimate, urban microenvironment and occupant behaviour upon overheating of Nordic apartment buildings
No full text| openaire: EC/H2020/856602/EU//FINEST TWINS Publisher Copyright: Copyright © 2025. Published by Elsevier B.V.Climate change has amplified the frequency and intensity of heatwaves in Nordic countries, inducing a strong thermal discomfort in buildings that are optimized for cold climates. Most existing studies either isolate urban microenvironment factors or building-scale dynamics, ignoring their combined impact on indoor thermal environments, while currently used climate datasets typically ignore differences at the district level. Aiming to fill this gap, this work combines the impact of urban microenvironmental factors, building characteristics, and occupant behaviour on indoor overheating during heatwaves in the Helsinki region. Validated computer simulations mapping temperature variations at room, apartment, building, and district scales, showed that greenery and building density can substantially reduce indoor temperatures by up to 1.2 °C. At the building and apartment scales, limited wind exposure and intensified solar radiation induce instead overheating in middle-floor apartments and on south-facing façades by up to 0.8 °C. Passive cooling measures and mechanical balanced ventilation can partially moderate overheating, whilst occupant behaviour crucially reduces the indoor temperatures by up to 4.5 °C through optimal balcony doors and windows operation. Active cooling is therefore essential, with required capacities ranging from 200–850 W per room, spiking to ∼2000 W in scenarios with suboptimal occupant behaviour. These specific findings offer actionable insights for urban planners, architects, and policymakers towards optimized building design, strategic urban planning, and integration of active cooling systems. The methodology here introduced, grounded on high-resolution weather data and comprehensive computer simulations, is general and can be easily applied to other climates as well.Peer reviewe
Image interpretation methods for highresolution scanning probe microscopy
No full textInterfaces play a central role in both natural and technological systems, and their properties are strongly influenced by atomic-scale interactions. Scanning probe microscopy (SPM), in particular scanning tunnelling microscopy (STM) and atomic force microscopy (AFM), has enabled direct imaging of surfaces and adsorbed molecules with atomic resolution. Advances in tip functionalization have further improved spatial resolution, revealing chemical bonds and molecular structures in great detail. However, interpretation of SPM images remains challenging: threedimensional adsorption geometries distort image contrast, and chemical identification often requires extensive quantum mechanical simulations.
This thesis addresses these challenges using both traditional and data-driven approaches. The traditional methodology is employed to support experimental studies of confined water dimers within molecular networks and of organosilicon compounds synthesized on surfaces. To overcome the limitations of manual structure identification, the thesis also develops machine learning models trained on simulated datasets for automated structure discovery for STM and AFM. These models show that machine learning can significantly reduce the time required for molecular identification.
The thesis highlights both the strengths and limitations of SPM simulation-based analysis and extends machine learning approaches in SPM to high-resolution STM imaging. While current models remain sensitive to noise and experimental artifacts, the results provide a step toward fully automated structure discovery.Rajapinnat ovat tärkeässä asemassa luonnollisissa ja teknologisissa järjestelmissä, ja atomitason vuorovaikutuksilla on niiden ominaisuuksiin suuri vaikutus. Tunnelointi- (engl. scanning tunnelling microscopy, STM) ja atomivoimamikroskooppi (engl. atomic force microscopy, AFM) ovat mahdollistaneet pintojen ja adsorboituneiden molekyylien suoran kuvantamisen yksittäisten atomien resoluutiolla. Mikroskoopin kärjen funktionalisoinnilla kuvan resoluutiota voidaan parantaa edelleen ja jopa yksittäisiä sidoksia atomien välillä voidaan kuvata. Näillä mittausmenetelmillä tuotetut kuvat ovat kuitenkin vaikeita tulkita: kolmiulotteiset adsorptioasennot vääristävät kontrastia, ja molekyylien kemiallinen tunnistus vaatii usein suuria kvanttimekaanisia simulointeja.
Tässä väitöskirjassa näitä haasteita lähestytään sekä perinteisin että dataohjautuvin menetelmin. Perinteistä lähestymistapaa käytetään tukemaan kokeellisia tutkimuksia, joissa tarkastellaan molekyyliverkostoon rajoittuneita vesimolekyylejä sekä piitä sisältävien orgaanisten molekyylien synteesiä. Perinteisen rakennetunnistuksen lisäksi väitöskirjassa kehitetään myös koneoppimismalleja, jotka on koulutettu simuloiduilla aineistoilla automaattiseen näytteiden tunnistamiseen STM- ja AFM-kuvista. Tulokset osoittavat, että koneoppiminen voi merkittävästi nopeuttaa molekyylien tunnistusta.
Väitöskirja tuo esiin perinteisen SPM-simulaatiomenetelmiin perustuvan näytteentunnistuksen vahvuuksia ja rajoitteita ja laajentaa koneoppimisen soveltamista erityisesti korkean resoluution STM-kuvien analyysiin. Vaikka nykyiset mallit ovat yhä herkkiä kohinalle ja kokeellisille artefakteille, tulokset ovat askel kohti näytteiden täysin automaattista tunnistusta.navigointi mahdollistastrukturell navigationstructural navigatio
Integrating FEM with reliability-based serviceability limit state design for a test embankment on low-carbon column-stabilised soil
No full textDry deep mixing (DDM) is one of the primary stabilisation methods used to reduce compressibility and increase bearing capacity of soft clays under road embankments. However, geotechnical designers encounter considerable uncertainty in designing DDM columns, arising from the properties of both soil and binder, as well as the stabilization techniques employed. These uncertainties are exacerbated when using novel binders, whose behaviour in real field conditions is still under investigation. In such cases, probabilistic simulation provides a systematic approach to quantifying and managing these uncertainties. This paper presents a reliability-based design framework that combines finite element (FE) analysis using the volume averaging technique and Monte Carlo simulation. The framework accounts for the uncertainty in the calculation of residual settlements of both soil and stabilised soil parameters in the case of a testing embankment built on low-carbon end-bearing and floating stabilised soil columns. The volume averaging technique approach was validated using field measurements and was found to reasonably capture the behavior of length-varying columns. Probabilistic results show that longer columns reduce significantly not only the residual settlement but also the range of plausible outcomes and the absolute variability. However, by linking failure probabilities with binder carbon emissions, an optimal length can be obtained, enabling sustainable design and reliability adjustments based on environmental and performance trades-offs. Lastly, a global sensitivity analysis on the risk of exceedance was performed to identify the influential parameters to target in soil investigations and requiring additional testing to reduce the probability of exceedance most effectively.Peer reviewe
A review on deep learning for vision-based hand detection, hand segmentation and hand gesture recognition in human–robot interaction
No full textHand-based analysis, including hand detection, segmentation, and gesture recognition, plays a pivotal role in enabling natural and intuitive human–robot interaction (HRI). Recent advances in vision-based deep learning (DL) have significantly improved robots’ ability to interpret hand cues across diverse settings. However, previous reviews have not addressed all three tasks collectively or focused on recent DL architectures. Filling this gap, we review recent studies at the intersection of DL and hand-based interaction in HRI. We structure the literature around three core tasks, i.e. hand detection, segmentation, and gesture recognition, highlighting DL models, dataset characteristics, evaluation metrics, and key challenges for each. We further examine the application of these models across industrial, assistive, social, aerial, and space robotics domains. We identify the dominant role of Convolutional and Recurrent Neural Networks (CNNs and RNNs), as well as emerging approaches such as attention-based models (Transformers), uncertainty-aware models, Graph Neural Networks (GNNs), and foundation models, i.e. Vision-Language Models (VLMs) and Large Language Models (LLMs). Our analysis reveals gaps, including the scarcity of HRI-specific datasets, underrepresentation of multi-hand and multi-user scenarios, limited use of RGBD and multi-modal inputs, weak cross-dataset generalization, and inconsistent real-time benchmarking. Dynamic and long-range gestures, multi-view setups, and context-aware understanding also remain relatively underexplored. Despite these limitations, promising directions have emerged, such as multi-modal fusion, use of foundation models for intent reasoning, and the development of lightweight architectures for deployment. This review offers a consolidated foundation to support future research on robust and context-aware DL systems for hand-centric HRI.Peer reviewe
3D-printed sensor electric circuits using atomic layer deposition
No full text3D-printing, also known as additive manufacturing, has enabled the production of dynamically shaped objects often customized for specific applications. Many applications, such as sensors in the aerospace industry, have demanding mass and volume requirements or need to work in challenging environments that necessitate electronics to be protected. The combination of 3D-printing and electronics could open up new applications not feasible previously. We propose a novel manufacturing method capable of integrating a complex electric circuit consisting of several, commonly available electronic components with a 3D-printed object. This is achieved using a commercial printer and atomic layer deposition for coating. Various printable polymers and coatings were tested to identify two polymers that could be printed into one object, allowing selective conductivity when coated with conductive coating. Selective conductivity is achieved when one polymer exhibits poorer and more non-continuous coating growth compared to the other. The 3D-printed object’s three-dimensional shape and details were used to create the electrical circuit and aid in achieving selective conductivity. A demonstration consisting of an ultraviolet light (UV) sensor, based on an existing traditional circuit board, was replicated using this method. The 3D-printed circuit was then tested by comparing its output with that of the original when placed under the same UV-light source. The novel circuit output closely followed the original. The presented method can combine an electric circuit with the dynamic capabilities of a 3D-printer, allowing for savings in existing applications as well as new applications.Peer reviewe
BaO-modified finger-like nickel-based anode for enhanced performance and durability of direct carbon solid oxide fuel cells
No full textPublisher Copyright: © 2024 Elsevier LtdDirect carbon solid oxide fuel cells (DC-SOFCs) are hopeful high-temperature energy conversion devices with all-solid-state structure, high efficiency, and low emission. The anode catalytic activity is a direct limiting factor in the electrochemical performance of DC-SOFCs. Here, we successfully fabricated a finger-like Ni-based anode/electrolyte in one step, followed by infiltrating BaO within the anode, which significantly improved the anodic reaction and DC-SOFC performance. At 850 °C, the BaO/Ni-YSZ anode-supported DC-SOFC gave the optimal output of 505 and 825 mW cm−2 powering by activated carbon and hydrogen, respectively, which were significantly superior to those of the cell with traditional Ni-YSZ anode. Moreover, DC-SOFC with BaO/Ni-YSZ anode exhibited more stable operation for 20.9 h under 100 mA at 850 °C, giving a relatively high fuel utilization of 23.4 %. These excellent performances can be partially attributed to the smaller particle sizes and more grain boundaries of the BaO/Ni-YSZ anode due to the BaO infiltration, which effectively enhanced the ionic conductivity and mechanical strength of the anode. More importantly, density functional theory simulation revealed that the infiltrated BaO in the Ni-YSZ anode enhanced the adsorption ability of Ni sites for carbon monoxide and oxygen ions, which led to the increased differential charge densities and the reduction in the energy barrier of electrochemical oxidation reaction, thus effectively improving DC-SOFC performance and conversion efficiency.Peer reviewe
The quantromon : A qubit-resonator system with orthogonal qubit and readout modes
No full textPublisher Copyright: © 2025 Author(s).The measurement of a superconducting qubit is implemented by coupling it to a resonator. The common choice is transverse coupling, which, in the dispersive approximation, introduces an interaction term that enables the measurement. This cross-Kerr term provides a qubit-state dependent dispersive shift in the resonator frequency with the device parameters chosen carefully to get sufficient signal while minimizing Purcell decay of the qubit. We introduce a two-mode circuit, nicknamed quantromon, with two orthogonal modes implementing a qubit and a resonator. Unlike before, where the coupling term emerges as a perturbative expansion, the quantromon has intrinsic cross-Kerr coupling by design. Our experiments implemented in a hybrid 2D-3D circuit QED architecture demonstrate some unique features of the quantromon like weak dependence of the dispersive shift on the qubit-resonator detuning and intrinsic Purcell protection. In a tunable qubit-frequency device, we show that the dispersive shift ( 2 χ / 2 π ) changes by only 0.8 MHz, while the qubit-resonator detuning ( Δ / 2 π ) is varied between 0.398 and 3.288 GHz. We also demonstrate Purcell protection in a second device where we tune the orthogonality between the two modes. Finally, we demonstrate a single-shot readout fidelity of 98.3%, which is comparable to the state-of-the-art measurements without the use of a parametric amplifier and suggests a potential simplification of the measurement circuitry for scaling up quantum processors.Peer reviewe
Learning-based adaptive neural control for safer navigation of unmanned surface vehicle with variable mass
No full textPublisher Copyright: © 2024This paper presents a novel approach to the precise control of variable mass unmanned surface vehicles (USVs) during payload deployment, where both mass and draught undergo unpredictable changes. We propose a draught observation method and an adaptive control strategy that leverages the strong coupling between the USV's motion states, mass, and draught. Our method employs a radial basis function neural network (RBF-NN) for real-time draught observation, using an offline training strategy based on gradient descent, combined with an adaptive online training strategy to improve observation accuracy. An adaptive control strategy based on the Backstepping method is then developed, incorporating real-time draught data from the RBF-NN to address unknown variations in mass and draught. The stability of both the RBF-NN observer and the adaptive control algorithm is rigorously verified using the Lyapunov method. Simulation results demonstrate that the proposed draught observation method achieves up to 30% faster convergence compared to traditional methods, with a significant improvement in observation accuracy. Furthermore, the adaptive control strategy effectively manages real-time adjustments in dynamic scenarios, maintaining robust control performance even under significant mass changes, where conventional approaches fail.Peer reviewe
A novel collaborative collision avoidance decision-making methodology based on potential collision areas for intelligent navigation
No full textPublisher Copyright: © 2024 Elsevier LtdShip navigation safety is a crucial component of intelligent navigation, and collaborative collision avoidance plays a vital role in ensuring safety and conflict resolution by enhancing decision-making efficiency in multi-ship encounters. In this paper, a collaborative collision avoidance model is proposed to connect encounter risk and decision-making in a more intuitive way. It pioneers the quantification of collision risk based on potential collision areas and differentiates encountered ships, and a collaborative mechanism is further constructed to formulate a collision avoidance decision-making model. International Regulations for Preventing Collisions at Sea (COLREGs) are comprehensively considered by differentiating risk relationships among ships and specify their roles and responsibilities. A ship collaboration mechanism under action intention games is constructed based on rational thinking to form a decision-making model with game-decision cycles. The results demonstrate that the model can meet the safety requirements in case studies, providing a rational reflection, and accurately determines encounter stages. The results also indicate that ship differentiation and role assignment can adapt to abnormal actions. This research makes significant contributions to ship decision support in term of better collaboration among ships while reducing action conflicts, promoting the development of intelligent navigation technologies.Peer reviewe
Dynamics collision risk evaluation and early alert in busy waters : A spatial-temporal coupling approach
No full textPublisher Copyright: © 2024 Elsevier LtdShip collision is a significant threat to waterborne transportation safety in busy waters. In order to mitigate the probability of ship collisions, improve navigation safety, and reduce economic losses, this paper proposes a spatial-temporal coupling approach to ship collision risk evaluation for alert in busy waters. First, the probabilistic ship domain considering ship characteristics (e.g., ship length and ship type) is determined based on historical Automatic Identification System (AIS) data. Secondly, the calculation models of the spatial-temporal collision risk of two-ship and multi-ship scenarios are proposed by the overlap of probabilistic ship domains, ship approaching trends, and catastrophe theory. Then, the ship probability domain overlap and the time required for ship collision avoidance are considered to estimate the threshold value of spatial-temporal collision risk between two ships. Meanwhile, a multi-level alert method for ship collision is proposed according to the degree of intrusion into the ship domain, the spatial-temporal collision risk, and the threshold value of the spatial-temporal risk collision. Finally, an example analysis was carried out based on the AIS data of the Luotou Channel in Zhoushan, China. The results indicate that the proposed method can effectively characterize the collision risk when two-ship or multi-ship encounters in busy waters, and can accurately classify the collision alert levels. Compared with traditional existing indicators, it is concluded that (1) the proposed method, which integrates both spatial and temporal collision risk, effectively categorizes collision alert levels, (2) it overcomes the limitations of traditional collision risk assessment methods, particularly in reflecting nonlinear changes in collision risk, and (3) it can assess collision risk in busy waters.Peer reviewe