部分的亜硝酸化型アナモックスプロセスと部分的脱窒型アナモックスプロセスの融合利用による豚ふん尿の高度脱窒

Tohoku University博士（環境科学）要約のみthesi

Hesitant Fuzzy Soft Set and Its Applications in Multicriteria Decision Making

Molodtsov’s soft set theory is a newly emerging mathematical tool to handle uncertainty. However, the classical soft sets are not appropriate to deal with imprecise and fuzzy parameters. This paper aims to extend the classical soft sets to hesitant fuzzy soft sets which are combined by the soft sets and hesitant fuzzy sets. Then, the complement, “AND”, “OR”, union and intersection operations are defined on hesitant fuzzy soft sets. The basic properties such as DeMorgan’s laws and the relevant laws of hesitant fuzzy soft sets are proved. Finally, with the help of level soft set, the hesitant fuzzy soft sets are applied to a decision making problem and the effectiveness is proved by a numerical example

geometrical vision measurement in mechanical engineering

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PPARγ as a Potential Target to Treat Airway Mucus Hypersecretion in Chronic Airway Inflammatory Diseases

Airway mucus hypersecretion (AMH) is a key pathophysiological feature of chronic airway inflammatory diseases such as bronchial asthma, cystic fibrosis, and chronic obstructive pulmonary disease. AMH contributes to the pathogenesis of chronic airway inflammatory diseases, and it is associated with reduced lung function and high rates of hospitalization and mortality. It has been suggested that AMH should be a target in the treatment of chronic airway inflammatory diseases. Recent evidence suggests that a key regulator of airway inflammation, hyperresponsiveness, and remodeling is peroxisome proliferator-activated receptor gamma (PPARγ), a ligand-activated transcription factor that regulates adipocyte differentiation and lipid metabolism. PPARγ is expressed in structural, immune, and inflammatory cells in the lung. PPARγ is involved in mucin production, and PPARγ agonists can inhibit mucin synthesis both in vitro and in vivo. These findings suggest that PPARγ is a novel target in the treatment of AMH and that further work on this transcription factor may lead to new therapies for chronic airway inflammatory diseases

Robust Full Waveform Inversion with deep Hessian deblurring

Full Waveform Inversion (FWI) is a technique widely used in geophysics to obtain high-resolution subsurface velocity models from waveform seismic data. Due to its large computation cost, most flavors of FWI rely only on the computation of the gradient of the loss function to estimate the update direction, therefore ignoring the contribution of the Hessian. Depending on the level of computational resources one can afford, an approximate of the inverse of the Hessian can be calculated and used to speed up the convergence of FWI towards the global (or a plausible local) minimum. In this work, we propose to use an approximate Hessian computed from a linearization of the wave-equation as commonly done in Least-Squares Migration (LSM). More precisely, we rely on the link between a migrated image and a doubly migrated image (i.e., an image obtained by demigration-migration of the migrated image) to estimate the inverse of the Hessian. However, instead of using non-stationary compact filters to link the two images and approximate the Hessian, we propose to use a deep neural network to directly learn the mapping between the FWI gradient (output) and its Hessian (blurred) counterpart (input). By doing so, the network learns to act as an approximate inverse Hessian: as such, when the trained network is applied to the FWI gradient, an enhanced update direction is obtained, which is shown to be beneficial for the convergence of FWI. The weights of the trained (deblurring) network are then transferred to the next FWI iteration to expedite convergence. We demonstrate the effectiveness of the proposed approach on two synthetic datasets and a field dataset

SSP-REGULARIZER: A STAR SHAPE PRIOR BASED REGULARIZER FOR VESSEL LUMEN SEGMENTATION IN OCT IMAGES

Optical coherence tomography (OCT) is widely used in high resolution imaging of biological tissues, which can help diagnose coronary heart disease by segmenting the vessellumen at the pixel-level. However, the lumen shape geometry is not well used in the state-of-the-art techniques for OCT image segmentation, especially the data-driven methods, leaving much room for performance improvement if some geometric features could be exploited to provide prior information. Thanks to the star shape geometry of vessel lumen, in this paper, a new Star Shape Prior based Regularizer (SSP-Regularizer) is proposed to improve segmentation performance. To validate its effectiveness, the proposed SSPRegularizer is applied to improve the optimization scheme used in Mask-RCNN for vessel lumen segmentation. Experimental results show that superior performance is achievedwith SSP-Regularizer, indicating its potentials in OCT imagery and optimization schemes

Effect of a combination of donepezil tablets and butylphthalide soft capsules on neurological function in dementia patients, and its effect on serum inflammatory factors

Purpose: To determine the effect of combined use of donepezil tablets and butylphthalide soft capsules in the treatment of patients with vascular dementia, and its effect on serum inflammatory factor levels and neurological functional recovery of patients.Methods: 120 patients with vascular dementia were selected and assigned to group A (n = 60) and group B (n = 60). All patients were treated with donepezil tablets, while patients in group A were, in addition, treated with butylphthalide soft capsules. Mini mental state examination (MMSE) scores, clinical dementia rating scale (CDRS) scores, activities of daily living (ADL) scores, incidence of adverse reactions, serum inflammatory factor levels and neurological functional recovery were determined.Results: There was significantly higher MMSE score in group A than in B, while CDRS score was lower in group A. The ADL scores and inflammatory factor levels were lower in group A than in B (p < 0.001), while neurological functional recovery was markedly better in A (p < 0.001). Incidents of unwanted events were comparable in groups A and B, and there were no serious complications in the patients.Conclusion: The combination therapy of donepezil tablets and butylphthalide soft capsules reduces inflammatory factor levels and improved cognitive level and quality of life of patients with vascular dementia. It also produces good neurological functional recovery and low incidence of adverse reactions. Therefore, this treatment strategy has potentials for the management of vascular dementia