nano-computed-tomography (nano-CT). They have emerged in many synchrotrons worldwide [1][2][3][4][5][6] and have been widely used in energy science where typical spatial resolutions of 30-60 nm and respective field of view (FOV) of about 40-70 μm are ideal for the ex situ or in situ characterization of different type of lithium-ion batteries. [7][8][9][10][11][12] However, they can be also utilized to characterize any type of materials like alloys, [13,14] rocks, [15] single minerals in solution, [16,17] polymers, [18] liquids, [19] biological tissues, [20] etc. While 19 nm spatial resolution has been reported in 2D on gold test patterns with long exposure, [21,22] existing TXMs operating with high brightness synchrotron sources currently provide a maximum resolution of 30 nm. [23] Projection microscopy, another full-field nano-CT technique, has the potential of achieving sub-20 nm spatial resolution. However, the best 3D resolution reported so far is 55 nm. [24] The constant and rapid development of manufactured nanomaterials and the societal and economic stakes associated with them are important drivers for improving the resolving power of X-ray microscopes.In the last decade, transmission X-ray microscopes (TXMs) have come into operation in most of the synchrotrons worldwide. They have proven to be outstanding tools for non-invasive ex and in situ 3D characterization of materials at the nanoscale across varying range of scientific applications. However, their spatial resolution has not improved in many years, while newly developed functional materials and microdevices with enhanced performances exhibit nanostructures always finer. Here, optomechanical breakthroughs leading to fast 3D tomographic acquisitions (85 min) with sub-10 nm spatial resolution, narrowing the gap between X-ray and electron microscopy, are reported. These new achievements are first validated with 3D characterizations of nanolithography objects corresponding to ultrahigh-aspect-ratio hard X-ray zone plates. Then, this powerful technique is used to investigate the morphology and conformality of nanometer-thick film electrodes synthesized by atomic layer deposition and magnetron sputtering deposition methods on 3D silicon scaffolds for electrochemical energy storage applications.

Most of the Deep Neural Networks (DNNs) based CT image denoising literature shows that DNNs outperform traditional iterative methods in terms of metrics such as the RMSE, the PSNR and the SSIM. In many instances, using the same metrics, the DNN results from low-dose inputs are also shown to be comparable to their high-dose counterparts. However, these metrics do not reveal if the DNN results preserve the visibility of subtle lesions or if they alter the CT image properties such as the noise texture.Accordingly, in this work, we seek to examine the image quality of the DNN results from a holistic viewpoint for low-dose CT image denoising. First, we build a library of advanced DNN denoising architectures. This library is comprised of denoising architectures such as the DnCNN, U-Net, Red-Net, GAN, etc. Next, each network is modeled, as well as trained, such that it yields its best performance in terms of the PSNR and SSIM. As such, data inputs (e.g. training patch-size, reconstruction kernel) and numeric-optimizer inputs (e.g. minibatch size, learning rate, loss function) are accordingly tuned. Finally, outputs from thus trained networks are further subjected to a series of CT bench testing metrics such as the contrast-dependent MTF, the NPS and the HU accuracy. These metrics are employed to perform a more nuanced study of the resolution of the DNN outputs' low-contrast features, their noise textures, and their CT number accuracy to better understand the impact each DNN algorithm has on these underlying attributes of image quality.

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This paper assesses the current remittance status and its impact on Nepalese economy. Secondary data from various sources were used for study. Statistical techniques including descriptive statistics and correlation was used. The number of Nepalese citizen for foreign employment is increasing year after year. Malaysia is the primary destination of Nepali migrants followed by Qatar, Saudi Arabia, UAE and others respectively. Nepal ranks 19 th position in top remittance receiving countries of the world and it ranks 4th position when remittance is compared as a percentage of GDP. Percentage increase in inflation was lower in comparison with the proportion of remittance as compared with GDP. Result showed insignificant relationship between remittance inflow and increase in agricultural land. Remittance has played several positive roles in Nepalese economy like reduction of poverty and unemployment, maintaining foreign exchange reserve and correcting balance of payments. Positive and significant correlation was found between GDP and remittance inflow per year at 10% level of significance. Remittance as compared with percentage of GDP and share of agriculture, forestry and fishing were negatively and significantly correlated. The share of agriculture, forestry and fishing to GDP of country was found to be diminishing but proportion of remittance when compared with GDP was increasing. Problem of labor shortage in agricultural as well as non-agricultural works is a genuine problem as active youths are involved in foreign employment. As volume of remittance is being increased rapidly, dependency of people on remittance is increasing and Nepalese economy is gradually becoming consumption oriented. Also, Nepalese economy is transforming from agriculture based economy to remittance based economy. So, formulation and implementation of appropriate policies which tap and utilize received remittance into productive sector is recommended.Remittance is that part of earning sent by individuals from migration destination to their home country or their place of origin, which is a vital source of foreign income for developing countries like Nepal. Although remittance can be sent in the form of kind but the term usually limited to represent monetary and cash transfers sent by international migrant workers to their place of origin [1]. Remittance covers large portion of financial flows to developing countries like Nepal. Remittance is important source in labour exporting countries to maintain foreign exchange reserve and to correct Balance of Payments. Remittance implies household income from foreign inflow mainly from momentary or permanent migration of people to those economies [2]. According to World Bank [3] remittance enables households to increase their level of consumption, ensure better health facilities, nutrition, education and other facilities.In developing countries, remittance is three times more than official development assistance [4] and it ranks second largest source of external finance after foreign direct investment [5...

In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoration, reconstruction, translation, as well as objective image quality assessment. Despite the impressive progress in generating high-resolution, perceptually realistic images, it is not clear if modern GANs reliably learn the statistics that are meaningful to a downstream medical imaging application. In this work, the ability of a state-of-the-art GAN to learn the statistics of canonical stochastic image models (SIMs) that are relevant to objective assessment of image quality is investigated. It is shown that although the employed GAN successfully learned several basic first-and second-order statistics of the specific medical SIMs under consideration and generated images with high perceptual quality, it failed to correctly learn several per-image statistics pertinent to the these SIMs, highlighting the urgent need to assess medical image GANs in terms of objective measures of image quality.