Microstrip Triplexer using a common triple-mode resonator

An all-resonator based triplexer is presented using a double-stub-loaded resonator (DSLR) that acts as a common resonator at the junction of the three channels. The open stub DSLR has been analysed using even and odd-mode method to reveal the relationship between the three resonant modes. The design offers flexibility of frequency selection. The DSLR resonator is coupled with three sets of hairpin resonators to form the triplexer at 1.8, 2.1, and 2.6 GHz for mobile communication applications. The measurement results are in very good agreement with the simulations

DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for Structural Connectivity Prediction from Brain fMRI

Mapping from functional connectivity (FC) to structural connectivity (SC) can facilitate multimodal brain network fusion and discover potential biomarkers for clinical implications. However, it is challenging to directly bridge the reliable non-linear mapping relations between SC and functional magnetic resonance imaging (fMRI). In this paper, a novel diffusision generative adversarial network-based fMRI-to-SC (DiffGAN-F2S) model is proposed to predict SC from brain fMRI in an end-to-end manner. To be specific, the proposed DiffGAN-F2S leverages denoising diffusion probabilistic models (DDPMs) and adversarial learning to efficiently generate high-fidelity SC through a few steps from fMRI. By designing the dual-channel multi-head spatial attention (DMSA) and graph convolutional modules, the symmetric graph generator first captures global relations among direct and indirect connected brain regions, then models the local brain region interactions. It can uncover the complex mapping relations between fMRI and structural connectivity. Furthermore, the spatially connected consistency loss is devised to constrain the generator to preserve global-local topological information for accurate intrinsic SC prediction. Testing on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the proposed model can effectively generate empirical SC-preserved connectivity from four-dimensional imaging data and shows superior performance in SC prediction compared with other related models. Furthermore, the proposed model can identify the vast majority of important brain regions and connections derived from the empirical method, providing an alternative way to fuse multimodal brain networks and analyze clinical disease.Comment: 12 page

Model-enhanced Vector Index

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with end-to-end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Model-enhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embedding-based models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions

Systematic investigation of single walled nanotubes production in mesoporous catalytic templates

The main goal of the project was to optimize the synthesis conditions for mesoporous materials like MCM-41 and SBA-15 in order to achieve their emerging applications in catalysis, reaction engineering, separation. By optimizing these parameters, it was expected to attain a specific and thorough understanding of the synthesis conditions of the mesoporous materials, with the prospect of industrial application in the near future.MASTER OF ENGINEERING (SCBE

Stability analysis of the amplifier with negative impedance compensation

A novel amplifier design technique based on negative impedance compensation has been proposed in our recent paper. In this paper, we investigate the stability of this amplifier system. The parameter space approach has been used to determine system parameters in the negative impedance circuit such that the stability of the amplifier system can be guaranteed in a certain region represented by those parameters. The simulation results have demonstrated that stable circuit behavior for the amplifier can be achieve

Joint Direction of Arrival-Polarization Parameter Tracking Algorithm Based on Multi-Target Multi-Bernoulli Filter

This paper presents a tracking algorithm for joint estimation of direction of arrival (DOA) and polarization parameters, which exhibit dynamic behavior due to the movement of signal source carriers. The proposed algorithm addresses the challenge of real-time estimation in multi-target scenarios with an unknown number. This algorithm is built upon the Multi-target Multi-Bernoulli (MeMBer) filter algorithm, which makes use of a sensor array called Circular Orthogonal Double-Dipole (CODD). The algorithm begins by constructing a Minimum Description Length (MDL) principle, taking advantage of the characteristics of the polarization-sensitive array. This allows for adaptive estimation of the number of signal sources and facilitates the separation of the noise subspace. Subsequently, the joint parameter Multiple Signal Classification (MUSIC) spatial spectrum function is employed as the pseudo-likelihood function, overcoming the limitations imposed by unknown prior information constraints. To approximate the posterior distribution of MeMBer filters, Sequential Monte Carlo (SMC) method is utilized. The simulation results demonstrate that the proposed algorithm achieves excellent tracking accuracy in joint DOA-polarization parameter estimation, whether in scenarios with known or unknown numbers of signal sources. Moreover, the algorithm demonstrates robust tracking convergence even under low Signal-to-Noise Ratio (SNR) conditions

Pulmonary lymphangioleimyomatosis and systemic lupus erythematosus in a menopausal woman

Abstract Background Pulmonary lymphangioleimyomatosis (PLAM) is a rare disease involving lung. PLAM primarily affects young women, a characteristic it shares with systemic lupus erythematosus (SLE). Estrogen has long been assumed to play an important role both in PLAM and SLE. We report a menopausal woman, who was found to have PLAM 1 year after she was diagnosed with SLE. Her chest radiograph was normal in the early phase of SLE. Case presentation A 52-year-old Chinese woman was referred to our hospital in August 2014 because of swelling in both legs. She also reported a malar rash and intermittent generalized arthralgia. Laboratory examination showed leukopenia. Her serum albumin level was 23 g/L; 24-h urinary protein excretion was 5.3 g. She tested positive for anti-Smith (Sm) antibody and anti-SS-A antibody. Renal biopsy indicated Class V + IV(G)-A lupus nephritis (LN). The condition of SLE and LN improved on a regime of tapering prednisolone and intermittent intravenous cyclophosphamide therapy until 1 year later when she developed exertional dyspnea accompanied with frequent cough. Thoracic computed tomography revealed numerous well-defined cysts and the diagnosis of PLAM was confirmed by lung biopsy. In the follow-up period, the patient continued to be on prednisolone and mycophenolate mofetil for the treatment of SLE, but only agreed to receive symptomatic treatment for PLAM. One year after the diagnosis of PLAM, during which time the SLE was stable, she died of respiratory failure and cor pulmonale. Conclusion We report a patient with coexisting SLE and PLAM, who was treated with immunosuppressive therapy. SLE was stable but PLAM was not improved. Although the coexistence of SLE and PLAM might be a coincidence, the occurrence of these two diseases in a menopausal woman may warrant further mechanistic exploration

Study on Water-Driving Law and Remaining Oil Distribution Pattern in Ultra-low Permeability Reservoir

The water-cut rises quickly and the remaining oil distribution is complex when the ultra-low permeability reservoir enters the high water cut stage. The comprehensive use of reservoir engineering, dynamic monitoring, numerical simulation, core experiments and other methods, this paper systematically summarizes three types of water-driving law and distribution characteristics of remaining oil, which are pore-fracture flow, pore-fracture flow and fracture flow. It is considered that the horizontal water drive is mainly controlled by material source, well pattern and fracture, while the vertical water drive is mainly controlled by reservoir heterogeneity, water line distance, injection-production well distance, etc. , the patterns of residual oil formed by different types of percolation are different, in this paper, 7 macroscopic patterns of remaining oil distribution, such as well pattern control, heterogeneity control, single sand body connected control, longitudinal interference, injection-production non-correspondence and non-main reservoir unutilized, are summarized, in the light of different remaining oil patterns, the paper puts forward the adjustment direction of tapping potential, such as optimizing injection-production structure, optimizing injection-production mode