Full waveform inversion procedures with irregular topography

Full waveform inversion (FWI) is a form of seismic inversion that uses data residual, found as the misfit, between the whole waveform of field acquired and synthesized seismic data, to iteratively update a model estimate until such misfit is sufficiently reduced, indicating synthetic data is generated from a relatively accurate model. The aim of the thesis is to review FWI and provide a simplified explanation of the techniques involved to those who are not familiar with FWI. In FWI the local minima problem causes the misfit to decrease to its nearest minimum and not the global minimum, meaning the model cannot be accurately updated. Numerous objective functions were proposed to tackle different sources of local minima. The ‘joint deconvoluted envelope and phase residual’ misfit function proposed in this thesis aims to combine features of these objective functions for a comprehensive inversion. The adjoint state method is used to generate an updated gradient for the search direction and is followed by a step-length estimation to produce a scalar value that could be applied to the search direction to reduce the misfit more efficiently. Synthetic data are derived from forward modelling involving simulating and recording propagating waves influenced by the mediums’ properties. The ‘generalised viscoelastic wave equation in porous media’ was proposed by the author in sub-chapter 3.2.5 to consider these properties. Boundary layers and conditions are employed to mitigate artificial reflections arising from computational simulations. Linear algebra solvers are an efficient tool that produces wavefield vectors for frequency domain synthetic data. Regions with topography require a grid generation scheme to adjust a mesh of nodes to fit into its non-quadrilateral shaped body. Computational co-ordinate terms are implemented within wave equations throughout topographic models where a single point in the model in physical domain are represented by cartesian nodes in the computational domains. This helps to generate an accurate and appropriate synthetic data, without complex modelling computations. Advanced FWI takes a different approach to conventional FWI, where they relax upon the use of misfit function, however none of their proponents claims the former can supplant the latter but suggest that they can be implemented together to recover the true model.Open Acces

Effective Volumetric Feature Modeling and Coarse Correspondence via Improved 3DSIFT and Spectral Matching

This paper presents a nonrigid coarse correspondence computation algorithm for volumetric images. Our matching algorithm first extracts then correlates image features based on a revised and improved 3DSIFT (I3DSIFT) algorithm. With a scale-related keypoint reorientation and descriptor construction, this feature correlation is less sensitive to image rotation and scaling. Then, we present an improved spectral matching (ISM) algorithm on correlated features to obtain a one-to-one mapping between corresponded features. One can effectively extend this feature correspondence to dense correspondence between volume images. Our algorithm can benefit nonrigid volumetric image registration in many tasks such as motion modeling in medical image analysis and processing

Flexible operation of large-scale coal-fired power plant integrated with solvent-based post-combustion CO2 capture based on neural network inverse control

Post-combustion carbon capture (PCC) with chemical absorption has strong interactions with coal-fired power plant (CFPP). It is necessary to investigate dynamic characteristics of the integrated CFPP-PCC system to gain knowledge for flexible operation. It has been demonstrated that the integrated system exhibits large time inertial and this will incur additional challenge for controller design. Conventional PID controller cannot effectively control CFPP-PCC process. To overcome these barriers, this paper presents an improved neural network inverse control (NNIC) which can quickly operate the integrated system and handle with large time constant. Neural network (NN) is used to approximate inverse dynamic relationships of integrated CFPP-PCC system. The NN inverse model uses setpoints as model inputs and gets predictions of manipulated variables. The predicted manipulated variables are then introduced as feed-forward signals. In order to eliminate steady-state bias and to operate the integrated CFPP-PCC under different working conditions, improvements have been achieved with the addition of PID compensator. The improved NNIC is evaluated in a large-scale supercritical CFPP-PCC plant which is implemented in gCCS toolkit. Case studies are carried out considering variations in power setpoint and capture level setpoint. Simulation results reveal that proposed NNIC can track setpoints quickly and exhibit satisfactory control performances

Gate-controlled reversible rectifying behaviour in tunnel contacted atomically-thin MoS2_{2} transistor

Atomically-thin 2D semiconducting materials integrated into van der Waals heterostructures have enabled architectures that hold great promise for next generation nanoelectronics. However, challenges still remain to enable their full acceptance as compliant materials for integration in logic devices. Two key-components to master are the barriers at metal/semiconductor interfaces and the mobility of the semiconducting channel, which endow the building-blocks of ${pn}$ diode and field effect transistor. Here, we have devised a reverted stacking technique to intercalate a wrinkle-free h-BN tunnel layer between MoS$_{2}$ channel and contacting electrodes. Vertical tunnelling of electrons therefore makes it possible to suppress the Schottky barriers and Fermi level pinning, leading to homogeneous gate-control of the channel chemical potential across the bandgap edges. The observed unprecedented features of ambipolar ${pn}$ to ${np}$ diode, which can be reversibly gate tuned, paves the way for future logic applications and high performance switches based on atomically thin semiconducting channel.Comment: 23 pages, 5 main figures + 9 SI figure

A Mendelian randomization study to assess the genetic liability of type 1 diabetes mellitus for IgA nephropathy

BackgroundThe prevalence of immunoglobulin A nephropathy (IgAN) seems to be higher in patients with type 1 diabetes mellitus (T1DM) than that in the general population. However, whether there exists a causal relationship between T1DM and IgAN remains unknown.MethodsThis study conducted a standard two-sample Mendelian randomization (MR) analysis to assess the causal inference by four MR methods, and the inverse variance-weighted (IVW) approach was selected as the primary method. To further test the independent causal effect of T1DM on IgAN, multivariable MR (MVMR) analysis was undertaken. Sensitivity analyses incorporating multiple complementary MR methods were applied to evaluate how strong the association was and identify potential pleiotropy.ResultsMR analyses utilized 81 single-nucleotide polymorphisms (SNPs) for T1DM. The evidence supports a significant causal relationship between T1DM and increased risk of IgAN [odds ratio (OR): 1.39, 95% confidence interval (CI): 1.10–1.74 for IVW, p < 0.05]. The association still exists after adjusting for triglyceride (TG), fasting insulin (FI), fasting blood glucose (FBG), homeostasis model assessment of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR), and glycated hemoglobin (HbA1c). MVMR analysis indicated that the effect of T1DM on IgAN vanished upon accounting for low-density lipoprotein cholesterol (LDL-c; OR: 0.97, 95% CI: 0.90–1.05, p > 0.05).ConclusionsThis MR study provided evidence that T1DM may be a risk factor for the onset of IgAN, which might be driven by LDL-c. Lipid-lowering strategies targeting LDL-c should be enhanced in patients with T1DM to prevent IgAN

Template synthesis of palladium nanotubes and their electrocatalytic properties

Palladium nanotubes were prepared by using silver nanowires as the template, which were prepared in a modified polyol reduction process. The morphology and structure of silver nanowires and palladium nanotubes were investigated by X-ray diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Electrochemical experimental data showed that palladium nanotubes displayed high electrocatalytic activity toward the electrooxidation of alcohols, especially for ethanol. The formation mechanism of palladium nanotubes as well as the relationship between their structure and electrocatalytic activity was discussed based on the experimental results

Performance of compact plastic scintillator strips with WLS-fiber and PMT/SiPM readout

This work presents the design and performance study of compact strips of plastic scintillator with WLS-fiber readout in a dimension of 0.1 * 0.02 * 2 m3, which evaluates as a candidate for cosmic-ray muon detector for JUNO-TAO. The strips coupling with 3-inch PMTs are measured and compared between the single-end and double-end readout options first, and the strip of double-end option coupling with SiPM is further measured and compared with the results of that with the PMTs. The performance of the strips determined by a detailed survey along their length with cosmic-ray muon after a detailed characterization of the used 3-inch PMTs and SiPMs.The proposed compact strip of plastic scintillator with WLS-fiber coupling with SiPM provides a good choice for cosmic-ray muon veto detector for limited detector dimension in particular

Impact of citalopram combined with mindfulness-based stress reduction on symptoms, cognitive functions and self-confidence in patients with depression

Purpose: To investigate the impact of the combination of citalopram and mindfulness-based stress reduction (MBSR) on the symptoms, cognitive functions and self-confidence of patients with depression.Methods: A total of 98 patients with depression were selected as study subjects and divided into combination therapy group (CT, n = 51) and conventional group (C, n = 47. The conventional group was treated with citalopram, while the combined group was treated with a combination of citalopram and MBSR. Depressive symptoms and self-confidence were evaluated using the 17-item Hamilton Depression Rating Scale (HAMD-17) and General Self-efficacy Scale (GSES). Cognitive functions were assessed by Wisconsin Card Sorting Test (WCST) and Trail Making Test (TMT). Changes in depressive symptoms, cognitive functions, self-confidence and clinical efficacies between the two groups were compared.Results: At weeks 1, 4 and 8 after treatment, CT group had lower HAMD-17 scores but higher GSES scores when compared with the conventional group (p < 0.05). In addition, CT group was superior to the conventional group in efficacy and overall response rate (100.00 vs. 85.11 %, p < 0.05). Also, CT group showed a shorter time of perseverative and non-perseverative errors on WCST and a shorter time for TMT-A and TMT-B, compared with the conventional group (p < 0.05).Conclusion: The combination therapy of citalopram and MBSR is effective in ameliorating depressive symptoms, and enhancing cognitive functions and self-confidence in patients with depression. These findings will increase the understanding of this combination therapy, and provide a clinical reference for the treatment of depression