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The growing complexity of urban energy systems, climate uncertainties, and geopolitical disruptions highlight the need for energy flexibility and smart management. Recent developments in smart buildings enable real-time adaptability and collective energy behavior through the deployment of Reinforcement Learning (RL), which optimizes energy use, integrates distributed resources, and enhances demand
Carbon Black (CB), graphene, and nanodiamonds represent three classes of manufactured carbonaceous nanoparticles consisting almost exclusively of insoluble material. Particle-related Reactive Oxygen Species (ROS) have been linked to in vivo and in vitro toxicity and determining particle ROS activity is an efficient way of estimating nanomaterial toxicity and assist in the safe-by-design developmen
Background: Air pollution is a known risk factor for chronic respiratory diseases, including asthma and chronic obstructive pulmonary disease (COPD). However, evidence regarding the role of black carbon (BC) remains mixed and sparse. This study aimed to examine the association of long-term exposure to air pollution, focusing on BC, with incident asthma and COPD in adults. Method: We followed 28,73
Background: Type 2 diabetes (T2D) is a well-established risk factor for cataract. However, whether the causal effect on cataract differs by sex remains unclear. Determining whether this causal effect differs by sex could help resolve discrepancies in epidemiological studies regarding cataract risk in populations with diabetes. Methods: Using summary statistics of East Asian and European population
Short-duration extreme rainfall can cause severe impacts in built environments and flood mitigation measures require high-resolution rainfall data to be effective. It is a particular challenge to observe convective storms, which are expected to intensify with climate change. However, rainfall monitoring networks operated by national meteorological and hydrological services generally have limited a
Six coumarins were isolated from two bioactive fractions of the 70% ethanol extract from the twigs and leaves of Wikstroemia indica (L.) C.A. Mey. (Thymelaeaceae), namely the ethyl acetate fraction (WIE) and the water fraction (WIW). These compounds were identified as umbelliferone (1), daphnoretin (2), daphnorin (3), adicardin (4), wikstronutin (5), and triumbelletin-7-O-β-D-glucoside (6). Compou
Aim: To model the long-term cost-effectiveness of scaling up two prevention interventions against type 2 diabetes mellitus (T2DM), i.e. community mobilisation through participatory learning and action (PLA) and mHealth mobile phone messaging, implemented in rural Bangladesh as part of the “DMagic” trial. Methods: A health-economic Markov model of the three-arm, cluster-randomised controlled DMagic
Access to detailed network data is often constrained by access-controlled datasets or by extensive manual labour. Using aerial imagery and a simplified OpenStreetMap road-level graph, a novel approach is proposed to extract lane-level road topology graphs at signalized intersections based on detection of common road objects, classification of lane attributes, and a simultaneous lane-to-lane connec
New results on continuous time nonlinear consensus under varying topology are presented. The results are proved utilizing non Lyapunov based methods, i.e., the Hilbert metric, showing the possibility of further investigation of Hilbert metric for consensus and synchronization problems.
In this paper we provide an analytical solution to an H2 optimal control problem, that applies whenever the process corresponds to a uniformly damped network of masses and springs. The solution covers both stable and unstable systems, and illustrates analytically how damping affects the levels of achievable performance. Furthermore, the resulting optimal controllers can be synthesised using passiv
When deploying deep neural networks on robots or other physical systems, the learned model should reliably quantify predictive uncertainty. A reliable uncertainty allows downstream modules to reason about the safety of its actions. In this work, we address metrics for uncertainty quantification. Specifically, we focus on regression tasks, and investigate Area Under Sparsification Error (AUSE), Cal
The Sensory Processing Measure, second edition (SPM-2), is an American assessment guiding person-centered sensory processing interventions, but it lacks Swedish adaptation for occupational therapists. In eight phases, this study translated the SPM-2 into Swedish and assessed its face validity and preliminary psychometric properties, including internal consistency and inter-scale/item correlations.
Atmospheric new particle formation (NPF) is a naturally occurring phenomenon, during which high concentrations of sub-10 nm particles are created through gas to particle conversion. The NPF is observed in multiple environments around the world. Although it has observable influence onto annual total and ultrafine particle number concentrations (PNC and UFP, respectively), only limited epidemiologic
This paper concerns control of a class of systems where multiple dynamically stable agents share a nonlinear and bounded control-interconnection. The agents are subject to a disturbance which is too large to reject with the available control action, making it impossible to stabilize all agents in their desired states. In this nonlinear setting, we consider two different anti-windup equipped propor
Social exclusion and individuals’ self-sufficiency have been on the public agenda in Sweden recently. One reason is the large influx of immigrants to Sweden in 2016. This paper aims to measure self-sufficiency over time, across the life cycle, depending on geographical origin, and time spent in Sweden to provide information about how self-sufficiency has developed over time and across the life cyc
This paper investigates the use of decentralised control architectures with heterogeneous dynamics for improving performance in large-scale systems. Our focus is on two well-known decentralised approaches; the 'predecessor following' and 'bidirectional' architectures for vehicle platooning. The former, utilising homogeneous control dynamics, is known to face exponential growth in disturbance ampli
Certainty equivalence adaptive controllers are analysed using a “data-driven Riccati equation”, corresponding to the model-free Bellman equation used in Q-learning. The equation depends quadratically on data correlation matrices. This makes it possible to derive simple sufficient conditions for stability and robustness to unmodeled dynamics in adaptive systems. The paper is concluded by short rema
The strategy of pre-training a large model on a diverse dataset, then fine-tuning for a particular application has yielded impressive results in computer vision, natural language processing, and robotic control. This strategy has vast potential in adaptive control, where it is necessary to rapidly adapt to changing conditions with limited data. Toward concretely understanding the benefit of pre-tr
The advances in highly automated and autonomous transportation systems over the last decade have generated great interest in topics in the safe navigation of land vehicles. With distributed control strategies employed in the majority of applications of autonomous vehicles, such as traffic and formation control, the much-required resilience takes the form of fault-tolerance with respect to informat
This letter presents and analyzes an adaptive data-driven controller that learns the optimal processing rate in a multi-unit processing network in the presence of disturbances. We formulate an optimization problem of linear cost, linear dynamics for the processing network model and an affine constraint on the dispatcher policy. A data-driven linear equation is constructed, based on which the onlin
