Chalmers Research
Not a member yet
88095 research outputs found
Sort by
Resource modelling in a circular economy context
Full text linkThe concept of the circular economy (CE) is increasingly suggested as a means of reducing environmental impacts and resource use of production and consumption through CE strategies like reuse, remanufacturing, and recycling. However, an increase in “circularity” does not guarantee an improvement in the use of resources over the lifecycle of a product. Transitions towards a more circular economy therefore need to be supported by quantitative assessments that can guide companies and policymakers in choosing appropriate strategies to implement. However, different methods vary in terms of their scope and focus, and therefore convey different types of information about the system investigated. This thesis aims to contribute towards improved knowledge about a number of methods that can evaluate how CE strategies affect resource use, specifically focusing on CE indicators and dynamic material flow analysis (MFA). The work presented builds on two studies. Article I is a review and mapping of the flows and processes that CE indicators capture in the product lifecycle. The indicators are also applied to a wide number of cases to determine how the indicators differ and to identify their potential limitations. In article II a dynamic MFA model is developed and applied to a multiple reuse and recycling case of lithium-ion batteries. The study examines how this circular solution could affect raw material and battery flows over time. Finally, the two studies are synthesised into a method comparison of CE indicators and dynamic MFA. The comparison focuses on similarities and differences between the methods with regards to the object of study, how temporal aspects are represented, system boundaries, and the type of results provided. While CE indicators provide information on variations of resource use over the product lifecycle, dynamic MFA informs on how CE strategies can affect stocks and flows of products and materials over time. Amongst other things, the results emphasise the importance of capturing the temporal dynamics of material flows when evaluating CE strategies, e.g. how the availability of secondary resources could change over time or assessing how a transition towards a more circular economy can play out over time
A monitoring campaign (2013-2020) of ESA\u27s Mars Express to study interplanetary plasma scintillation
Full text linkThe radio signal transmitted by the Mars Express (MEX) spacecraft was observed regularly between the years 2013-2020 at X-band (8.42 GHz) using the European Very Long Baseline Interferometry (EVN) network and University of Tasmania\u27s telescopes. We present a method to describe the solar wind parameters by quantifying the effects of plasma on our radio signal. In doing so, we identify all the uncompensated effects on the radio signal and see which coronal processes drive them. From a technical standpoint, quantifying the effect of the plasma on the radio signal helps phase referencing for precision spacecraft tracking. The phase fluctuation of the signal was determined for Mars\u27 orbit for solar elongation angles from 0 to 180 deg. The calculated phase residuals allow determination of the phase power spectrum. The total electron content of the solar plasma along the line of sight is calculated by removing effects from mechanical and ionospheric noises. The spectral index was determined as which is in agreement with Kolmogorov\u27s turbulence. The theoretical models are consistent with observations at lower solar elongations however at higher solar elongation ($ ]]>160 deg) we see the observed values to be higher. This can be caused when the uplink and downlink signals are positively correlated as a result of passing through identical plasma sheets
E-Band Low-Loss Reconfigurable Phase Shifters
No full textTwo types of low-loss mechanically reconfigurable phase shifters at E-band based on gap waveguide (GW) technology, the GW slow wave and the GW width-variation, are proposed in this letter. Phase shifts are achieved by mechanical movements. To avoid wave leakage and undesired resonances during the movement, quasi-PEC-AMC structures are implemented. The phase shifters have been fabricated and measured. The results show that the insertion losses are about 0.4 and 0.3 dB, and the reflection coefficients are better than 13 and 15 dB for the slow wave and the width variation, respectively, over 71-86 GHz
Regularised semi-parametric composite likelihood intensity modelling of a Swedish spatial ambulance call point pattern
Full text linkMotivated by the development of optimal dispatching strategies for prehospital resources, we model the spatial distribution of ambulance call events in the Swedish municipality Skellefte\ue5 during 2014–2018 in order to identify important spatial covariates and discern hotspot regions. Our large-scale multivariate data point pattern of call events consists of spatial locations and marks containing the associated priority levels and sex labels. The covariates used are related to road network coverage, population density, and socio-economic status. For each marginal point pattern, we model the associated intensity function by means of a log-linear function of the covariates and their interaction terms, in combination with lasso-like elastic-net regularized composite/Poisson process likelihood estimation. This enables variable selection and collinearity adjustment as well as reduction of variance inflation from overfitting and bias from underfitting. To incorporate mobility adjustment, reflecting people’s movement patterns, we also include a nonparametric (kernel) intensity estimate as an additional covariate. The kernel intensity estimation performed here exploits a new heuristic bandwidth selection algorithm. We discover that hotspot regions occur along dense parts of the road network. A mean absolute error evaluation of the fitted model indicates that it is suitable for designing prehospital resource dispatching strategies. Supplementary materials accompanying this paper appear online
Advancing systems biology of yeast through machine learning and comparative genomics
Full text linkSynthetic biology has played a pivotal role in accomplishing the production of high value commodities, pharmaceuticals, and bulk chemicals. Fueled by the breakthrough of synthetic biology and metabolic engineering, Saccharomyces cerevisiae and various other yeasts (such as Yarrowia lipolytica, Pichia pastoris) have been proven to be promising microbial cell factories and are frequently used in scientific studies. However, the cellular metabolism and physiological properties for most of the yeast species have not been characterized in detail. To address these knowledge gaps, this thesis aims to leverage the large amounts of data available for yeast species and use state-of-the-art machine learning techniques and comparative genomic analysis to gain a deeper insight into yeast traits and metabolism.In this thesis, machine learning was applied to various unresolved biological problems on yeasts, i.e., gene essentiality, enzyme turnover number (kcat), and protein production. In the first part of the work, machine learning approaches were employed to predict gene essentiality based on sequence features and evolutionary features. It was demonstrated that the essential gene prediction could be substantially improved by integrating evolution-based features. Secondly, a high-quality deep learning model DLKcat was developed to predict kcat\ua0values by combining a graph neural network for substrates and a convolutional neural network for proteins. By predicting kcat profiles for 343 yeast/fungi species, enzyme-constrained models were reconstructed and used to further elucidate the cellular metabolism on a large scale. Lastly, a random forest algorithm was adopted to investigate feature importance analysis on protein production, it was found that post-translational modifications (PTMs) have a relatively higher impact on protein production compared with amino acid composition. In comparative genomics, a comprehensive toolbox HGTphyloDetect was developed to facilitate the identification of horizontal gene transfer (HGT) events. Case studies on some yeast species demonstrated the ability of HGTphyloDetect to identify horizontally acquired genes with high accuracy. In addition, through systematic evolution analysis (e.g., HGT, gene family expansion) and genome-scale metabolic model simulation, the underlying mechanisms for substrate utilization were further probed across large-scale yeast species
Thermodynamic Performance of Hot-Carrier Solar Cells: A Quantum Transport Model
Full text linkIn conventional solar cells, photogenerated carriers lose part of their energy before they can be extracted to make electricity. The aim of hot-carrier solar cells is to extract the carriers before this energy loss, thereby turning more energy into electrical power. This requires extracting the carriers in a nonequilibrium (nonthermal) energy distribution. Here, we investigate the performance of hot-carrier solar cells for such nonequilibrium distributions. We propose a quantum transport model in which each energy-loss process (carrier thermalization, relaxation, and recombination) is simulated by a B\ufcttiker probe. We study charge and heat transport to analyze the hot-carrier solar cell\u27s power output and efficiency, introducing partial efficiencies for different loss processes and the carrier extraction. We show that producing electrical power from a nonequilibrium distribution has the potential to improve the output power and efficiency. Furthermore, in the limit where the distribution is thermal, we prove that a boxcar-shaped transmission for the carrier extraction maximizes the efficiency at any given output power
Assessing the potential to use serious gaming in planning processes for sanitation designed for resource recovery
Full text linkThere is an urgent need for innovations in the sanitation sector to minimize environmental impacts and maximize resource recovery. Uptake of innovations may require changes in established technical practices, organisational norms and/or individual behaviours. Achieving change in any of these areas requires influencing cognitive, normative and relational learning processes. Serious games have been identified a potential tool for planners and environmental managers to influence such learning processes. This study designed the serious game RECLAIM to share knowledge about resource recovery from sanitation and to support attitude-change and collaboration between players. A structured framework was applied to assess if the game: 1) increased understanding of resource recovery (cognitive learning), 2) changed worldviews (normative learning), 3) led to more collaboration (relational learning), and 4) was a positive experience. Proof-of-concept testing of the game in Uganda found that it was positively received. The game provided cognitive learning on environmental and health impacts, resource recovery, and sanitation in general. Players gained an appreciation of the need for collaboration and it was deemed to have the potential to influence worldviews of a larger stakeholder group. Future recommendations include embedding the game in planning processes, including several gaming sessions that would strengthen cognition learning and the potential for changing practices
Gapwaveguide Automotive Imaging Radar Antenna with Launcher in Package Technology
Full text linkA 77 GHz gapwaveguide radar antenna system with launcher-in-package (LiP) technology is presented in this paper for automotive imaging applications. Firstly, state-of-the-art LiP technology integrated with radar transceivers is proposed. The transceivers are equipped with waveguide interfaces for RF connection, enabling direct integration with waveguide antennas. Robust interconnects for coupling transceivers to waveguide antennas with non-galvanic contacts are proposed using gapwaveguide packaging technology. A simultaneous multi-mode imaging radar system using 4 cascaded aforementioned transceivers is introduced. Designated antenna elements of the system are realized by slot arrays with center-fed ridge gapwaveguides. Ultimately, the imaging radar antenna has a top radiating slot layer, a middle distribution layer and a bottom interconnect layer capable of accommodating 4 LiP radar transceivers with considerable assembly tolerance which is really one of the key aspects for commercial automotive radar applications. Input matching and radiation patterns of the antenna are verified by measurement. The results indicate that the proposed gapwaveguide imaging radar antenna in conjunction with the novel LiP packaging is able to serve the radar system properly. To the best of the authors’ knowledge, the proposed gapwaveguide antenna system is the first imaging radar antenna system ever developed for LiP components. This work provides a compact, high-efficiency and cost-effective solution for the integration of complex radar systems with waveguide antennas
On motion resistance estimation and modeling for heterogeneous road vehicles
Full text linkClimate change is driving the development of CO2 reducing technologies within the transportation industry. One of the most promising technologies is battery electric vehicles. However, the combination of limited battery capacity, relatively long charging times and few charging stations makes them more vulnerable to conditions when energy consumption is higher than usual compared to vehicles driven by fossil fuel. This thesis focuses on vehicle and environment attributes that create energy-consuming forces resisting the vehiclemotion, i.e. the motion resistance and how to model and estimate them.The method developed in the thesis is based on a separation principle where attributes affecting the motion resistance are separated into vehicle, road and weather characteristics. This enables using vehicle data from heterogeneous vehicles to estimate local road weather conditions. The method is validated using simulations and real vehicle experiments.The results show that the road and weather conditions can be estimated using data from connected vehicles and energy consumption of heavy-duty vehicle combinations is largely affected by crosswinds. Furthermore, the motion resistance from crosswinds can be characterized by simple models with only a few tuning parameters.The main conclusions from this work are that road weather conditions including crosswinds need to be accounted for in range estimation algorithms, road weather estimates based on connected vehicle data is a promising technique, and windy days need to be anticipated in advance to avoid potential charging chaos