Miracle-dz/UniFAR-FTUs huggingface dataset:
BEHAVIOR OF SPACE CHARGE DOMINATED BEAM ENVELOPE IN CENTRAL REGION OF HIGH CURRENT CYCLOTRON

In this paper the space charge effect in the two first turn after injection has been investigated. In order to determine beam envelopes, two corresponding equations were chosen. In addition, all steps of calculation were done by MATLAB program. It should be mentioned limiting current and also magnetic, electrical field and edge effect has been considered. As far as, the high current cyclotron with 0. 8π mm mrad emittance has been studied and current alters till 10 mA.
1H NMR and Calorimetric Studies of the Inclusion of Trimethylammonium Cations into Water Soluble Calixresorcinarenes

Abstract The complexation of tetramethylammonium (TEMA), benzyltrimethylammonium (BTMA), p-nitrobenzyltrimethylammonium (BTMAN) and N, N, N-trimethylanilinium (TMA) by the tetrasulphonate derivative of the resorcinol cyclic tetramer (1), was studied in aqueous solution by 1H NMR and calorimetry. Host 1 specifically recognizes the—N+ (CH3)3 group of TEMA, BTMA and BTMAN, whereas it binds TMA unselectively; TMA is included both via the charged group and the aromatic moiety. The binding constants of all four guests with 1, as determined by both 1H NMR and calorimetric titrations, show that all inclusion processes are almost equally stabilised. ΔHΔ and ΔSΔ values, determined by direct calorimetry, reveal specific interactions that are not expressed in the ΔGΔ terms and indicate that we are dealing with “non-classical hydrophobic effects”. The effects of the structural, conformational and electronic properties of the guests on the forces driving the inclusion processes are discussed.
Insulin resistance and oxidative stress in diabetic rats treated with flaxseed oil

Hyperglycemia is a metabolic disorder that results in excessive production of free radicals which leads to severe oxidative damage of cell components like lipids, proteins and DNA. Supplementation with flaxseed oil has been reported to reduce oxidative stress. Eighty male albino rats were used in this study, and were divided randomly into four groups: control, flaxseed, diabetic and treated groups. Blood glucose and plasma insulin were measured, and insulin resistance was calculated. Membrane cholesterol, triglycerides and total lipids were estimated. Also, membrane advanced oxidation protein products (AOPP), malondialdehyde (MDA) and superoxide dismutase (SOD) were determined. Urinary 8hydroxyguanosine was estimated by high performance liquid chromatography (HPLC). The data showed that flaxseed oil significantly decreased fasting blood glucose which may be related to significant decrease in insulin resistance. Also, there was a decrease in oxidative stress parameters indicating the beneficial effect of flaxseed oil in scavenging free radicals. Positive correlation was found between insulin resistance and oxidative stress parameters (MDA and 8-OHdG). We concluded that, flaxseed oil administration has a beneficial effect on decreasing insulin resistance in diabetic rats through the scavenging of free radicals and increasing antioxidant enzymes.
Characterization and In Vivo Evaluation of an Inclusion Complex of Oridonin and 2-hydroxypropyl-β-cyclodextrin

Oridonin, a diterpenoid, is a sparingly soluble compound and its aqueous solubility can't meet the requirement of clinical intravenous administration. This study was, accordingly, to prepare an inclusion complex of oridonin and 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) by lyophilization to improve its apparent solubility. The solubility enhancement of oridonin was evaluated by phase solubility method, and the phase solubility curve displayed a typical AL-type, indicating the formation of 1:1 inclusion complex. The formation of inclusion complex was confirmed by DSC, XRD, FTIR, and NMR, and thereby two possible inclusion modes were inferred. In vivo studies demonstrated that HP-β-CD had no significant effect on the plasma pharmacokinetic behaviors of oridonin following i.v. administration to rats, but the inclusion complex tended to decrease the distribution of oridonin in heart, spleen, and kidney and increase that in lung in mice, compared to that of free drug.
Kinetics of protein folding: Nucleation mechanism, time scales, and pathways

The kinetics and thermodynamics of protein folding is investigated using low friction Langevin simulation of minimal continuum mode of proteins. We show that the model protein has two characteristic temperatures: (a) Tθ, at which the chain undergoes a collapse transition from an extended conformation; (b) Tf(< Tθ), at which a finite size first‐order transition to the folded state takes place. The kinetics of approach to the native state from initially denatured conformations is probed by several novel correlation functions. We find that the overall kinetics of approach to the native conformation occurs via a three‐stage multiple pathway mechanism. The initial stage, characterized by a series of local dihedral angle transitions, eventually results in the compaction of the protein. Subsequently, the molecule acquires native‐like structures during the second stage of folding. The final stage of folding involves activated transitions from one of the native‐like structures to the native conformation. The first two stages are characterized by a multiplicity of pathways while relatively few paths are involved in the final stage. A detailed analysis of the dynamics of individual trajectories reveals a novel picture of protein folding. We find that afraction of the initial population reaches the native conformation without the formation of any detectable intermediates. This pathway is associated with a nucleation mechanism, i.e., once a critical number of tertiary contacts are established then the native state is reached rapidly. The remaining fraction of molecules become trapped in misfolded structures (stabilized by incorrect tertiary contacts). The slow folding involves transitions over barriers from these structures to the native conformation. The theoretical predictions are compared with recent experiments that probe protein folding kinetics by hydrogen exchange labeling technique. © 1995 John Wiley & Sons, Inc.
Resonant behavior and microfluidic manipulation of silicone cilia due to an added mass effect

In a microfluidic device, cilia are good candidates to generate complex flow in solution by stirring the microscale fluid. To realize the potential as a novel microfluidic system, it is essential to understand the resonant behavior of cilia in solution and optimize the performance of a cilia-based device. In this paper, the resonant behavior of polydimethylsiloxane (PDMS) cilia in water is investigated by a novel computational method in order to understand the underlying physics and to suggest the optimal design of a cilia-based device. The resonant frequency of a single cilium in water is quantitatively compared to both analytical solutions and experimental results considering an added mass effect. Also, to propose the optimal design of cilia-based device as a microfluidic mixer, the fluid velocity and the pressure in multiple cilia are analysed according to the spacing between neighboring cilia. When the spacing increases from 100 µm to 600 µm, the flow pattern generated by the tips of cilia is changed from a large vortex to multiple small vortexes, which can be utilized for transport, separation and reaction of biomolecules. The particle dispersion with the mixing efficiency in multiple cilia is analysed to predict the performance as a microfluidic device.
A comparison of silver ion HPLC plus GC with Fourier‐transform IR spectroscopy for the determination of trans double bonds in unsaturated fatty acids

A rapid procedure has been developed for the isolation of methyl esters of trans-monoenoic fatty acids by silver ion high-performance liquid chromato- graphy. The combined saturated plus trans-monoene fraction was collected for analysis by gas chromatography and comparison with the composition of the unfractionated sample. The trans-monoene contents of partially hydrogenated vegetable oils, determined in this way, are compared with data obtained by Fourier-transform infrared spectroscopy. Alternative methods of base-line correction are discussed. The infrared technique gave values that were a little higher than the chromatographic ones, presumably because the former also detected trans unsaturation in di- and polyunsaturated fatty acids.
THE VARIABILITY OF THE ANTIOXIDANT CAPACITY OF RED WINES IN RELATION WITH GRAPEVINE VARIETY

The red wine is one of the foods rich in antioxidants, compounds that protect the organisms against the destructive action of the free radicals on the health. In this context, for the promotion of wines on markets, an identification of the wines assortments with high antioxidant capacity should be achieved. The present study, performed during 2012– 2014 period, at Research and Development Institute for Viticulture and Enology Valea Calugareasca, aimed to define the antioxidant capacity of the wines made from 11 grapevine varieties belonging to the three groups of wines assortments and to make a correlation of the antioxidant capacity with the phenolic potential of the grapes and wines. Depending on the period when the grapevine variety was created, an old wine assortment, a current and a newly created one can be differentiated. The antioxidant capacity of the wine was performed by means the TEAC method, the phenolic potential of the grapes by Glories method and the phenolic composition of the wines by OIV spectrophotometric methods. The data processing was realized by using the Regression and correlation analysis. The obtained results showed that the varieties 'Negru aromat', 'Novac' and 'Olivia' are characterized by a high antioxidant capacity, the group formed by 'Feteasca neagra', 'Pinot noir' and 'Merlot' showed a medium antioxidant capacity. The lowest values of the antioxidant capacity were registered in the case of 'Negru moale', 'Negru vartos', 'Blauerzweigelt' and 'Burgund' mare varieties. The antioxidant capacity of the wines was closely correlated with the following parameters of the phenolic potential of the raw material: total polyphenols, extractable anthocyanins, tannins originated from seeds and the maturity of the seeds.
A general theory for anisotropic Kirchhoff-Love shells with embedded fibers and in-plane bending

In this work we present a generalized Kirchhoff-Love shell theory that can capture anisotropy not only in stretching and out-of-plane bending, but also in in-plane bending. This setup is particularly suitable for heterogeneous and fibrous materials such as textiles, biomaterials, composites and pantographic structures. The presented theory is a direct extension of existing Kirchhoff-Love shell theory to incorporate the in-plane bending resistance of fibers. It also extends existing high gradient Kirchhoff-Love shell theory for initially straight fibers to initially curved fibers. To describe the additional kinematics of multiple fiber families, a so-called in-plane curvature tensor – which is symmetric and of second order – is proposed. The effective stress tensor and the in-plane and out-of-plane moment tensors are then identified from the mechanical power balance. These tensors are all second order and symmetric for general materials. The constitutive equations for hyperelastic materials are derived from different expressions of the mechanical power balance. The weak form is also presented as it is required for computational shell formulations based on rotation-free finite element discretizations.
Data for: Free-Breathing Water, Fat, R2∗ and B0 Field Mapping of the Liver Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (MERLOT)

Purpose: To achieve free-breathing quantitative fat and R∗ 2 mapping of the liver using a generalized model-based iterative reconstruction, dubbed as MERLOT. Methods: For acquisition, we use a multi-echo radial FLASH sequence that acquires multiple echoes with different complementary radial spoke encodings. We investigate real-time single-slice and volumetric multi-echo radial FLASH acquisition. For the latter, the sampling scheme is extended to a volumetric stack-of-stars acquisition. Model-based reconstruction based on generalized nonlinear inversion is used to jointly estimate water, fat, R∗ 2, B0 field inhomogeneity, and coil sensitivity maps from the multicoil multi-echo radial spokes. Spatial smoothness regularization is applied onto the B0 field and coil sensitivity maps, whereas joint sparsity regularization is employed for the other parameter maps. The method integrates calibration-less parallel imaging and compressed sensing and was implemented in BART. For the volumetric acquisition, the respiratory motion is resolved with self-gating using SSA-FARY. The quantitative accuracy 0Submitted to Magnetic Resonance in Medicine. 1 ar X iv :2 10 1. 02 78 8v 1 [ ph ys ic s. m ed -p h] 7 J an 2 02 1 of the proposed method was validated via numerical simulation, the NIST phantom, a water/fat phantom, and in in-vivo liver studies. Results: For real-time acquisition, the proposed model-based reconstruction allowed acquisition of dynamic liver fat fraction and R∗ 2 maps at a temporal resolution of 0.3 s per frame. For the volumetric acquisition, whole liver coverage could be achieved in under 2 minutes using the selfgated motion-resolved reconstruction. Conclusion: The proposed multi-echo radial sampling sequence achieves fast k -space coverage and is robust to motion. The proposed model-based reconstruction yields spatially and temporally resolved liver fat fraction, R∗ 2 and B0 field maps at high undersampling factor and with volume coverage.
Towards High Data-Rate Diffusive Molecular Communications: Performance Enhancement Strategies

Diffusive molecular communications (DiMC) have recently gained attention as a candidate for nano- to micro- and macro-scale communications due to its simplicity and energy efficiency. As signal propagation is solely enabled by Brownian motion mechanics, DiMC faces severe inter-symbol interference (ISI), which limits reliable and high data-rate communications. Herein, recent literature on DiMC performance enhancement strategies is surveyed; key research directions are identified. Signaling design and associated design constraints are presented. Classical and novel transceiver designs are reviewed with an emphasis on methods for ISI mitigation and performance-complexity tradeoffs. Key parameter estimation strategies such as synchronization and channel estimation are considered in conjunction with asynchronous and timing error robust receiver methods. Finally, source and channel coding in the context of DiMC is presented.
Superflares, Chromospheric Activities, and Photometric Variabilities of Solar-type Stars from the Second-year Observation of TESS and Spectra of LAMOST

In this work, 1272 superflares on 311 stars are collected from 22,539 solar-type stars from the second-year observation of the Transiting Exoplanet Survey Satellite (TESS), which almost covered the northern hemisphere of the sky. Three superflare stars contain hot Jupiter candidates or ultrashort-period planet candidates. We obtain γ = −1.76 ± 0.11 of the correlation between flare frequency and flare energy ( ) for all superflares and get β = 0.42 ± 0.01 of the correlation between superflare duration and energy (T duration ∝ E β ), which supports that a similar mechanism is shared by stellar superflares and solar flares. Stellar photometric variability (R var) is estimated for all solar-type stars, and the relation of is included. An indicator of chromospheric activity (S-index) is obtained by using data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) for 7454 solar-type stars. Distributions of these two properties indicate that the Sun is generally less active than superflare stars. We find that saturation-like feature of R var ∼ 0.1 may be the reason for superflare energy saturating around 1036 erg. Object TIC 93277807 was captured by the TESS first-year mission and generated the most energetic superflare. This superflare is valuable and unique in that it can be treated as an extreme event, which may be generated by different mechanisms than other superflares.
Slow manifolds within network dynamics encode working memory efficiently and robustly

Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation. Here, we use a top-down modeling approach to examine network-level mechanisms of working memory, an enigmatic issue and central topic of study in neuroscience. We optimize thousands of recurrent rate-based neural networks on a working memory task and then perform dynamical systems analysis on the ensuing optimized networks, wherein we find that four distinct dynamical mechanisms can emerge. In particular, we show the prevalence of a mechanism in which memories are encoded along slow stable manifolds in the network state space, leading to a phasic neuronal activation profile during memory periods. In contrast to mechanisms in which memories are directly encoded at stable attractors, these networks naturally forget stimuli over time. Despite this seeming functional disadvantage, they are more efficient in terms of how they leverage their attractor landscape and paradoxically, are considerably more robust to noise. Our results provide new hypotheses regarding how working memory function may be encoded within the dynamics of neural circuits.
Quantum Earth Mover's Distance: A New Approach to Learning Quantum Data

Quantifying how far the output of a learning algorithm is from its target is an essential task in machine learning. However, in quantum settings, the loss landscapes of commonly used distance metrics often produce undesirable outcomes such as poor local minima and exponentially decaying gradients. As a new approach, we consider here the quantum earth mover’s (EM) or Wasserstein-1 distance, recently proposed in [De Palma et al. , arXiv:2009.04469] as a quantum analog to the classical EM distance. We show that the quantum EM distance possesses unique properties, not found in other commonly used quantum distance metrics, that make quantum learning more stable and eﬃcient. We propose a quantum Wasserstein generative adversarial network (qW-GAN) which takes advantage of the quantum EM distance and provides an eﬃcient means of performing learning on quantum data. Our qWGAN requires resources polynomial in the number of qubits, and our numerical experiments demonstrate that it is capable of learning a diverse set of quantum data.
An Information-theoretic Progressive Framework for Interpretation

Both brain science and the deep learning communities have the problem of interpreting neural activity. For deep learning, even though we can access all neurons’ activity data, interpretation of how the deep network solves the task is still challenging. Although a large amount of effort has been devoted to interpreting a deep network, there is still no consensus of what interpretation is. This paper tries to push the discussion in this direction and proposes an information-theoretic progressive framework to synthesize interpretation. Firstly, we discuss intuitions of interpretation: interpretation is meta-information; interpretation should be at the right level; inducing independence is helpful to interpretation; interpretation is naturally progressive; interpretation doesn’t have to involve a human. Then, we build the framework with an information map splitting idea and implement it with the variational information bottleneck technique. After that, we test the framework with the CLEVR dataset. The framework is shown to be able to split information maps and synthesize interpretation in the form of meta-information.
High-NA optical edge detection via optimized multilayer films

There has been a significant effort to design nanophotonic structures that process images at the speed of light. A prototypical example is in edge detection, where photonic-crystal-, metasurface-, and plasmon-based designs have been proposed and in some cases experimentally demonstrated. In this work, we show that multilayer optical interference coatings can achieve visible-frequency edge detection in transmission with high numerical aperture, two-dimensional image formation, and straightforward fabrication techniques, unique among all nanophotonic approaches. We show that the conventional Laplacian-based transmission spectrum may not be ideal once the scattering physics of real designs is considered, and show that better performance can be attained with alternative spatial filter functions. Our designs, comprising alternating layers of Si and SiO2 with total thicknesses of only ≈1 μm , demonstrate the possibility for optimized multilayer films to achieve state-of-the-art edge detection, and, more broadly, analog optical implementations of linear operators.
A Novel Regression Loss for Non-Parametric Uncertainty Optimization

Quantification of uncertainty is one of the most promising approaches to establish safe machine learning. Despite its importance, it is far from being generally solved, especially for neural networks. One of the most commonly used approaches so far is Monte Carlo dropout, which is computationally cheap and easy to apply in practice. However, it can underestimate the uncertainty. We propose a new objective, referred to as second-moment loss (SML), to address this issue. While the full network is encouraged to model the mean, the dropout networks are explicitly used to optimize the model variance. We intensively study the performance of the new objective on various UCI regression datasets. Comparing to the state-of-the-art of deep ensembles, SML leads to comparable prediction accuracies and uncertainty estimates while only requiring a single model. Under distribution shift, we observe moderate improvements. As a side result, we introduce an intuitive Wasserstein distance-based uncertainty measure that is non-saturating and thus allows to resolve quality differences between any two uncertainty estimates.
TODG: Distributed Task Offloading With Delay Guarantees for Edge Computing

Edge computing has been an efficient way to provide prompt and near-data computing services for resource-and-delay sensitive IoT applications via computation offloading. Effective computation offloading strategies need to comprehensively cope with several major issues, including 1) the allocation of dynamic communication and computational resources, 2) delay constraints of heterogeneous tasks, and 3) requirements for computationally inexpensive and distributed algorithms. However, most of the existing works mainly focus on part of these issues, which would not suffice to achieve expected performance in complex and practical scenarios. To tackle this challenge, in this paper, we systematically study a distributed computation offloading problem with delay constraints, where heterogeneous computational tasks require continually offloading to a set of edge servers via a limiting number of stochastic communication channels. The task offloading problem is formulated as a delay-constrained long-term stochastic optimization problem under unknown prior statistical knowledge. To solve this problem, we first provide a technical path to transform and decompose it into several slot-level sub-problems. Then, we devise a distributed online algorithm, namely TODG, to efficiently allocate resources and schedule offloading tasks. Further, we present a comprehensive analysis for TODG in terms of the optimality gap, the worst-case delay, and the impact of system parameters. Extensive simulation results demonstrate the effectiveness and efficiency of TODG.