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Experiments and Lattice-Boltzmann Simulation of Flow in a Vertically Aligned Gearbox
No full textThis paper presents a study of the oil flow in a vertically arranged FZG gearbox. The splash and churning losses are experimentally evaluated using measurements of the resistance torque. Using high speed imaging, the instantaneous oil splashing inside the gearbox is also presented and compared with Computational Fluid Dynamics (CFD) results from the Lattice-Boltzmann method (LBM), instead of the traditional grid-based finite volume method. Four different configurations, including a spur gear based on the standard FZG geometry and a disc pair wheel-pinion with the same tip diameters of the standard geometries are used. The experiments cover a range from 500 to 3000 rpm and three oil levels are studied. For the CFD simulations, the same oil levels and rotational speeds are used. The experimental results indicate torque differences depending on the oil level and the configuration. The splashing pattern is also different from the standard horizontal FZG case, which is typically studied in the literature. On the other hand, the CFD simulations and flow visualization experiments are in relative agreement with one another. The similarities and differences in the torque values for the different configurations and the splashing pattern for both experiments and CFD simulations are analyzed and discussed
Multimode character of quantum states released from a superconducting cavity
Get PDFQuantum state transfer by propagating wave packets of electromagnetic radiation requires tunable couplings between the sending and receiving quantum systems and the propagation channel or waveguide. The highest fidelity of state transfer in experimental demonstrations so far has been in superconducting circuits. Here, the tunability always comes together with nonlinear interactions, arising from the same Josephson junctions that enable the tunability. The resulting nonlinear dynamics correlates the photon number and spatiotemporal degrees of freedom and leads to a multimode output state, for any multiphoton state. In this work, we study as a generic example the release of complex quantum states from a superconducting resonator, employing a flux tunable coupler to engineer and control the release process. We quantify the multimode character of the output state and discuss how to optimize the fidelity of a quantum state transfer process with this in mind
Influence of building envelope on indoor air quality: Field measurements, analysis, and method development related to indoor odors
Get PDFThe ventilation system should provide occupants with fresh air while removing excess pollutants from the building. However, increasing ventilation may inadvertently draw more pollutants into the occupant area or prove ineffective in altering emission rates from building materials and furnishings. If not addressed properly, this can make raising the ventilation rate inefficient, resulting in unnecessary heat losses or, in the worst case, reduced indoor air quality. This thesis addresses two previously insufficiently understood situations of contaminant transport within buildings, both manifested as unpleasant indoor smells: contaminant transport from adjacent compartments and early-stage emissions of air pollutants in new buildings.The former, inspired by school buildings in Sweden demolished due to \u27moldy\u27 smells, was thoroughly explored in my Licentiate thesis, Contaminant Transport by Air Infiltration from Crawl Space to Occupant Area-Numerical Simulations and Field Measurements in Swedish schools, and is presented here as a summary.The latter focuses on indoor air quality in new buildings, which often have initial high volatile organic compound (VOC) levels, typically perceived as a \u27new smell.\u27 In Sweden, it is common to run the ventilation system at full rate for several months as a remedy due to the negative effects of high VOC levels on occupants. However, the drawback of this strategy is the risk for over-ventilation with unnecessary energy losses. Two methods, ‘VOC-passport’ and ‘Ventilation threshold’, are developed to assess how ventilation can improve indoor air quality in more energy-efficient ways.Results show that with VOC-passport, it is possible to simulate dynamic variations in VOC concentrations in new buildings based on passive VOC measurements and building physics modeling. With this method, it is possible to find an optimal ventilation strategy for low VOC concentrations and minimal energy losses. In addition, an analytical analysis of the diffusion of VOCs in materials shows that if ventilation rates exceed a certain threshold, further increases will not affect the emission rate. A quantified ventilation threshold is useful for setting the ventilation rate regarding optimal off-gassing and an important complement to the VOC-passport
Differential Responders to a Mixed Meal Tolerance Test Associated with Type 2 Diabetes Risk Factors and Gut Microbiota—Data from the MEDGI-Carb Randomized Controlled Trial
Get PDFThe global prevalence of type 2 diabetes mellitus (T2DM) has surged in recent decades, and the identification of differential glycemic responders can aid tailored treatment for the prevention of prediabetes and T2DM. A mixed meal tolerance test (MMTT) based on regular foods offers the potential to uncover differential responders in dynamical postprandial events. We aimed to fit a simple mathematical model on dynamic postprandial glucose data from repeated MMTTs among participants with elevated T2DM risk to identify response clusters and investigate their association with T2DM risk factors and gut microbiota. Data were used from a 12-week multi-center dietary intervention trial involving high-risk T2DM adults, comparing high- versus low-glycemic index foods within a Mediterranean diet context (MEDGICarb). Model-based analysis of MMTTs from 155 participants (81 females and 74 males) revealed two distinct plasma glucose response clusters that were associated with baseline gut microbiota. Cluster A, inversely associated with HbA1c and waist circumference and directly with insulin sensitivity, exhibited a contrasting profile to cluster B. Findings imply that a standardized breakfast MMTT using regular foods could effectively distinguish non-diabetic individuals at varying risk levels for T2DM using a simple mechanistic model
Localization and interaction of interlayer excitons in MoSe2/WSe2 heterobilayers
Get PDFTransition metal dichalcogenide (TMD) heterobilayers provide a versatile platform to explore unique excitonic physics via the properties of the constituent TMDs and external stimuli. Interlayer excitons (IXs) can form in TMD heterobilayers as delocalized or localized states. However, the localization of IX in different types of potential traps, the emergence of biexcitons in the high-excitation regime, and the impact of potential traps on biexciton formation have remained elusive. In our work, we observe two types of potential traps in a MoSe2/WSe2 heterobilayer, which result in significantly different emission behavior of IXs at different temperatures. We identify the origin of these traps as localized defect states and the moir\ue9 potential of the TMD heterobilayer. Furthermore, with strong excitation intensity, a superlinear emission behavior indicates the emergence of interlayer biexcitons, whose formation peaks at a specific temperature. Our work elucidates the different excitation and temperature regimes required for the formation of both localized and delocalized IX and biexcitons and, thus, contributes to a better understanding and application of the rich exciton physics in TMD heterostructures
Practical battery State of Health estimation using data-driven multi-model fusion
Get PDFDue to dynamic vehicle operating conditions, random user behaviors, and cell-to-cell variations, accurately estimating the battery state of health (SoH) is challenging. This paper proposes a data-driven multi-model fusion method for battery capacity estimation under arbitrary usage profiles. Six feasible and mutually excluded scenarios are meticulously categorized to cover all operating conditions. Four machine learning (ML) algorithms are individually trained using time-series data to estimate the current time step battery capacity. Additionally, a prediction model based on the histogram data is adopted from previous work to predict the next step capacity value. Then, a Kalman filter (KF) is applied to fuse all the estimation and prediction results systematically. The developed method has been demonstrated on cells operated under diverse profiles to verify its effectiveness and practicability
Predictors of preparedness for recovery following colorectal cancer surgery: a latent class trajectory analysis
Get PDFAim: With an interest in providing knowledge for person-centred care, our overall goal is to contribute a greater understanding of diversity among patients in terms of their preparedness before and up to six months after colorectal cancer surgery. Our aim was to describe and provide a tentative explanation for differences in preparedness trajectory profiles. Material and methods: The study was explorative and used prospective longitudinal data from a previously published intervention study evaluating person-centred information and communication. The project was conducted at three hospitals in Sweden. Patient-reported outcomes measures, including the Longitudinal Preparedness for Colorectal Cancer Surgery Questionnaire, were collected before surgery, at discharge, and four to six weeks, three months, and six months after surgery. Clinical data were retrospectively obtained from patients’ medical records. We used latent class growth models (LCGMs) to identify latent classes that distinguish subgroups of patients who represent different preparedness trajectory profiles. To determine the most plausible number of latent classes, we considered statistical information about model fit and clinical practice relevance. We used multivariable regression models to identify variables that explain the latent classes. Results: The sample (N = 488) comprised people with a mean age of 68 years (SD = 11) of which 44% were women. Regarding diagnoses, 60% had colon cancer and 40% rectal cancer. The LCGMs identified six latent classes with different preparedness for surgery and recovery trajectories. The latent classes were predominantly explained by differences in age, sex, physical classification based on comorbidities, treatment hospital, global health status, distress, and sense of coherence (comprehensibility and meaningfulness). Conclusion: Contrary to the received view that emphasizes standardized care practices, our results point to the need for adding person-centred and tailored approaches that consider individual differences in how patients are prepared before and during the recovery period related to colorectal cancer surgery
When IC meets text: Towards a rich annotated integrated circuit text dataset
No full textAutomated Optical Inspection (AOI) is a process that uses cameras to autonomously scan printed circuit boards for quality control. Text is often printed on chip components, and it is crucial that this text is correctly recognized during AOI, as it contains valuable information. In this paper, we introduce \textit{ICText}, the largest dataset for text detection and recognition on integrated circuits. Uniquely, it includes labels for character quality attributes such as low contrast, blurry, and broken. While loss-reweighting and Curriculum Learning (CL) have been proposed to improve object detector performance by balancing positive and negative samples and gradually training the model from easy to hard samples, these methods have had limited success with one-stage object detectors commonly used in industry. To address this, we propose Attribute-Guided Curriculum Learning (AGCL), which leverages the labeled character quality attributes in \textit{ICText}. Our extensive experiments demonstrate that AGCL can be applied to different detectors in a plug-and-play fashion to achieve higher Average Precision (AP), significantly outperforming existing methods on \textit{ICText} without any additional computational overhead during inference. Furthermore, we show that AGCL is also effective on the generic object detection dataset Pascal VOC. Our code and dataset will be publicly available at \href{https://github.com/chunchet-ng/ICText-AGCL}{https://github.com/chunchet-ng/ICText-AGCL}
When IC meets text: Towards a rich annotated integrated circuit text dataset
No full textAutomated Optical Inspection (AOI) is a process that uses cameras to autonomously scan printed circuit boards for quality control. Text is often printed on chip components, and it is crucial that this text is correctly recognized during AOI, as it contains valuable information. In this paper, we introduce \textit{ICText}, the largest dataset for text detection and recognition on integrated circuits. Uniquely, it includes labels for character quality attributes such as low contrast, blurry, and broken. While loss-reweighting and Curriculum Learning (CL) have been proposed to improve object detector performance by balancing positive and negative samples and gradually training the model from easy to hard samples, these methods have had limited success with one-stage object detectors commonly used in industry. To address this, we propose Attribute-Guided Curriculum Learning (AGCL), which leverages the labeled character quality attributes in \textit{ICText}. Our extensive experiments demonstrate that AGCL can be applied to different detectors in a plug-and-play fashion to achieve higher Average Precision (AP), significantly outperforming existing methods on \textit{ICText} without any additional computational overhead during inference. Furthermore, we show that AGCL is also effective on the generic object detection dataset Pascal VOC. Our code and dataset will be publicly available at \href{https://github.com/chunchet-ng/ICText-AGCL}{https://github.com/chunchet-ng/ICText-AGCL}
Novel Technique for High-Frequency Carrier Envelope Offset Frequency Detection using Vernier Microcombs
No full textWe demonstrate a novel Vernier dual-comb based method for the detection and division of a microcomb\u27s carrier envelope of fset frequency that is beyond the bandwidth of typical commercial detection electronics