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# Fusion

15.10.2021
17:31 Fast flows prevent buildup of impurities on the edge of tokamak plasmas

Impurities in the plasmas in fusion tokamaks can reduce performance. These impurities are created by interactions between the hot plasma and the metal tokamak walls. These walls are often armored with tungsten. This material resists heat, but degrades over time, releasing impurities into the plasma. Simulations predict how these impurities behave, but they are difficult to measure directly because many impurities only emit weak levels of radiation. The experiment detailed here used a collector probe to sample the plasma. It determined that detrimental impurities accumulate in the region just outside the plasma edge only when the tokamak magnetic fields rotate around the donut-shaped tokamak in a counter-clockwise direction. This is the opposite direction that the plasma current moves. Clockwise rotation causes fast flows that remove the impurities.

17:31 Working to make measurements of plasma disruption mitigation methods more accurate

A team of fusion researchers at the Department of Energy's Oak Ridge National Laboratory used datasets from measurements on the Joint European Torus, or JET, tokamak to model an improved method for quantifying the amount of plasma-radiated power during a disruption of normal operations.

13:34 The Weekend Playlist: A sprawling epic from Toronto jazz trio BadBadNotGood, a shot of nostalgia from synth-pop revivalists Magdalena Bay and more

Keeping up with new music releases can be a difficult task. Your Weekend Playlist offers a brief introduction to a broad range of the most interesting new tracks and emerging artists. This week’s playlist features new music from BadBadNotGood, Magdalena Bay, Bizarrap and a collaboration between Hamilton Leithauser and Kevin Morby. Plus, new music from Cate Le Bon and Roc Marciano. Click here for the Spotify link. BadBadNotGood: Signal From The Noise In a recent podcast, music critic Kelefa Sanneh describes how, in the late 60s and early 70s, two groups —The Velvet Underground and Yes — offered competing visions for the future of rock and roll. Of course, The Velvet Underground’s vision proved triumphant — their laid-back and loose sound became the template and a key influence for punk music, alt-rock and indie. As for the prog-rock/jazz fusion wizards in Yes, despite their technical prowess and grand ambitions, the music today sounds

09:45 Global Stochastic Optimization of Stellarator Coil Configurations. (arXiv:2110.07464v1 [physics.plasm-ph])

In the construction of a stellarator, the manufacturing and assembling of the coil system is a dominant cost. These coils need to satisfy strict engineering tolerances, and if those are not met the project could be canceled as in the case of the National Compact Stellarator Experiment (NCSX) project [25]. Therefore, our goal is to find coil configurations that increase construction tolerances without compromising the performance of the magnetic field. In this paper, we develop a gradient-based stochastic optimization model which seeks robust stellarator coil configurations in high dimensions. In particular, we design a two-step method: first, we perform an approximate global search by a sample efficient trust-region Bayesian optimization; second, we refine the minima found in step one with a stochastic local optimizer. To this end, we introduce two stochastic local optimizers: BFGS applied to the Sample Average Approximation and Adam, equipped with a control variate for variance

14.10.2021
23:27 The world's first working nuclear fusion reactor could be coming soon near your town: Five UK sites including Ardeer, Moorside and Severn Edge have been shortlisted for a plant – with operations as early as 2040

The UK's first prototype fusion power plant is a step closer to reality today, after five sites were shortlisted as the potential home for the pioneering technology. Ardeer in North Ayrshire, Goole in Yorkshire, Moorside in Cumbria, Ratcliffe-on-Soar in Nottinghamshire and Severn Edge in Gloucestershire are all vying to be the location for what could be the world's first working nuclear fusion reactor. The UK's first prototype fusion power plant is a step closer to reality today, after five sites were shortlisted as the potential home for the pioneering technology. Ardeer in North Ayrshire, Goole in Yorkshire, Moorside in Cumbria, Ratcliffe-on-Soar in Nottinghamshire and Severn Edge in Gloucestershire are all vying to be the location for what could be the world's first working nuclear fusion reactor.

21:24 The world's first working nuclear fusion reactor could be coming soon near your town: Five UK sites including Ardeer, Moorside and Severn Edge have been shortlisted for a plant – with operations as early as 2040

The UK's first prototype fusion power plant is a step closer to reality today, after five sites were shortlisted as the potential home for the pioneering technology.

12.10.2021
14:23 Publisher Correction: Demonstration of reduced neoclassical energy transport in Wendelstein 7-X

Continuous developments in Additive Manufacturing (AM) technologies are opening opportunities in novel machining, and improving design alternatives for modern particle accelerator components. One of the most critical, complex, and delicate accelerator elements to manufacture and assemble is the Radio Frequency Quadrupole (RFQ) linear accelerator, used as an injector for all large modern proton and ion accelerator systems. For this reason, the RFQ has been selected by a wide European collaboration participating in the AM developments of the I.FAST (Innovation Fostering in Accelerator Science and Technology) Horizon 2020 project. RFQ is as an excellent candidate to show how sophisticated pure-copper accelerator components can be manufactured by AM and how their functionalities can be boosted by this evolving technology. To show the feasibility of the AM process, a prototype RFQ section has been designed, corresponding to one-quarter of a 750 MHz 4-vane RFQ, which was optimised for

11.10.2021
06:32 Construction Cost Index Forecasting: A Multi-feature Fusion Approach. (arXiv:2108.10155v2 [cs.LG] UPDATED)

The construction cost index is an important indicator of the construction industry. Predicting CCI has important practical significance. This paper combines information fusion with machine learning, and proposes a multi-feature fusion (MFF) module for time series forecasting. Compared with the convolution module, the MFF module is a module that extracts certain features. Experiments have proved that the combination of MFF module and multi-layer perceptron has a relatively good prediction effect. The MFF neural network model has high prediction accuracy and efficient prediction efficiency. At the same time, MFF continues to improve the potential of prediction accuracy, which is a study of continuous attention.

06:32 MilliTRACE-IR: Contact Tracing and Temperature Screening via mm-Wave and Infrared Sensing. (arXiv:2110.03979v1 [eess.SP])

In this work, we present milliTRACE-IR, a joint mm-wave radar and infrared imaging sensing system performing unobtrusive and privacy preserving human body temperature screening and contact tracing in indoor spaces. Social distancing and fever detection have been widely employed to counteract the COVID-19 pandemic, sparking great interest from academia, industry and public administrations worldwide. While most solutions have dealt with the two aspects separately, milliTRACE-IR combines, via a robust sensor fusion approach, mm-wave radars and infrared thermal cameras. The system achieves fully automated measurement of distancing and body temperature, by jointly tracking the faces of the subjects in the thermal camera image plane and the human motion in the radar reference system. It achieves decimeter-level accuracy in distance estimation, inter-personal distance estimation (effective for subjects getting as close as 0.2 m), and accurate temperature monitoring (max. errors of 0.5 C).

05:59 Multi-fidelity information fusion with concatenated neural networks. (arXiv:2110.04170v1 [physics.flu-dyn])

Recently, computational modeling has shifted towards the use of deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a concatenated neural network approach to build more tailored, effective, and efficient machine learning models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the

05:59 A conservative implicit-PIC scheme for the hybrid kinetic-ion fluid-electron plasma model on curvilinear meshes. (arXiv:2110.03886v1 [physics.plasm-ph])

The hybrid kinetic-ion fluid-electron plasma model is widely used to study challenging multi-scale problems in space and laboratory plasma physics. Here, a novel conservative scheme for this model employing implicit particle-in-cell techniques is extended to arbitrary coordinate systems via curvilinear maps from logical to physical space. The scheme features a fully non-linear electromagnetic formulation with a multi-rate time advance - including sub-cycling and orbit-averaging for the kinetic ions. By careful choice of compatible particle-based kinetic-ion and mesh-based fluid-electron discretizations in curvilinear coordinates, as well as particle-mesh interpolations and implicit midpoint time advance, the scheme is proven to conserve total energy for arbitrary curvilinear meshes. In the electrostatic limit, the method is also proven to conserve total momentum for arbitrary curvilinear meshes. Although momentum is not conserved for arbitrary curvilinear meshes in the electromagnetic

08.10.2021
16:45 Researchers propose novel permanent magnet design methods for quasi-axisymmetric stellarator

A new permanent magnet design of quasi-axisymmetric stellarator was made by researchers led by Prof. Xu Guosheng from the Hefei Institutes of Physical Science (HFIPS) of the Chinese Academy of Sciences.

05.10.2021
08:33 The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics. (arXiv:2110.01177v1 [eess.AS])

The Second DiCOVA Challenge aims at accelerating the research in diagnosing COVID-19 using acoustics (DiCOVA), a topic at the intersection of acoustics signal processing, machine learning, and healthcare. This challenge is an open call to researchers to analyze a dataset of audio recordings, collected from individuals with and without COVID-19, for a two-class classification. The development set audio recordings correspond to breathing, cough, and speech sound samples collected from 965 (172 COVID) individuals. The challenge features four tracks, one associated with each sound category and a fourth fusion track allowing experimentation with combination of the individual sound categories. In this paper, we introduce the challenge and provide a detailed description of the task and a baseline system.

08:10 The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics. (arXiv:2110.01177v1 [eess.AS])

The Second DiCOVA Challenge aims at accelerating the research in diagnosing COVID-19 using acoustics (DiCOVA), a topic at the intersection of acoustics signal processing, machine learning, and healthcare. This challenge is an open call to researchers to analyze a dataset of audio recordings, collected from individuals with and without COVID-19, for a two-class classification. The development set audio recordings correspond to breathing, cough, and speech sound samples collected from 965 (172 COVID) individuals. The challenge features four tracks, one associated with each sound category and a fourth fusion track allowing experimentation with combination of the individual sound categories. In this paper, we introduce the challenge and provide a detailed description of the task and a baseline system.

04.10.2021
03:54 Discharge modeling in EAST using bidirectional LSTM. (arXiv:2110.00346v1 [physics.plasm-ph])

An improved discharge model based on a bidirectional neural network was developed. The bidirectional LSTM model was used, and it was trained by the experimental data from the Experimental Advanced Superconducting Tokamak (EAST) campaign 2010-2020 discharges. Compared to our previous works (Chenguang Wan et al 2021 Nucl. Fusion \textbf{61} 066015), the present work reproduces the discharge evolution process through more key diagnostic signals, including the electron density $n_{e}$, store energy $W_{mhd}$, loop voltage $V_{loop}$, actual plasma current $I_{p}$, normalized beta $\beta_{n}$, toroidal beta $\beta_{t}$, beta poloidal $\beta_{p}$, elongation at plasma boundary $\kappa$, internal inductance $l_{i}$, q at magnetic axis $q_{0}$, and q at 95% flux surface $q_{95}$. The similarity of electron density $n_{e}$ and loop voltage $V_{loop}$ is improved by 1%, and 5%. The average similarity of all the selected key diagnostic signals between modeling results and the experimental data

03:54 On the role of density fluctuations in the core turbulent transport of Wendelstein 7-X. (arXiv:2110.00277v1 [physics.plasm-ph])

A recent characterization of core turbulence carried out with a Doppler reflectometer in the optimized stellarator Wendelstein 7-X (W7-X) found that discharges achieving high ion temperatures at the core featured an ITG-like suppression of density fluctuations driven by a reduction of the gradient ratio $\eta_i = L_n/L_{T_i}$ [D. Carralero et al., Nucl. Fusion, 2021]. In order to confirm the role of ITG turbulence in this process, we set out to establish experimentally the relation between core density fluctuations, turbulent heat flux and global confinement. With this aim, we consider the scenarios found in the previous work and carry out power balance analysis for a number of representative ones, including some featuring high ion temperature. As well, we evaluate the global energy confinement time and discuss it in the context of the ISS04 inter-stellarator scaling. We find that, when turbulence is suppressed as a result of a reduction of $\eta_i$, there is a reduction of ion

01.10.2021
17:11 The race is on to replicate the power of the sun with fusion energy

With a number of recent breakthroughs, the race is on to prove that nuclear fusion is not only possible, but integral to a clean energy future

04:38 Physics design point of high-field stellarator reactors. (arXiv:2109.15189v1 [physics.plasm-ph])

The ongoing development of electromagnets based on High Temperature Superconductors has led to the conceptual exploration of high-magnetic-field fusion reactors of the tokamak type, operating at on-axis fields above 10 T. In this work we explore the consequences of the potential future availability of high-field three-dimensional electromagnets on the physics design point of a stellarator reactor. We find that, when an increase in the magnetic field strength $B$ is used to maximally reduce the device linear size $R\sim B^{-4/3}$ (with otherwise fixed magnetic geometry), the physics design point is largely independent of the chosen field strength/device size. A similar degree of optimization is to be imposed on the magnetohydrodynamic, transport and fast ion confinement properties of the magnetic configuration of that family of reactor design points. Additionally, we show that the family shares an invariant operation map of fusion power output as a function of the auxiliary power and

04:38 Effects of collisional ion orbit loss on neoclassical tokamak radial electric fields. (arXiv:2109.14758v1 [physics.plasm-ph])

Ion orbit loss is considered important for the radial electric field $E_r$ in tokamak edge plasmas. In neoclassical equilibria, collisions can scatter ions onto loss orbits and generate a steady-state radial current. The latter could potentially drive the edge $E_r$ away from its neoclassical value. To quantitatively measure this effect, an ion-orbit-flux diagnostic has been implemented in the axisymmetric version of the gyrokinetic particle-in-cell code XGC. The validity of the diagnostic is demonstrated by studying the collisional relaxation of $E_r$ in the core plasma. After this verification, the ion orbit-loss effect is numerically measured in the edge for an H-mode plasma in DIII-D geometry. In quasisteady state, the edge $E_r$ is found to be mainly determined by the radial ion force balance condition, in which the radial electric force on ions balances the large ion pressure gradient associated with the given density pedestal. In this quasisteady state, the collisional

29.09.2021
10:32 Turning old models fashion again: Recycling classical CNN networks using the Lattice Transformation. (arXiv:2109.13885v1 [cs.CV])

In the early 1990s, the first signs of life of the CNN era were given: LeCun et al. proposed a CNN model trained by the backpropagation algorithm to classify low-resolution images of handwritten digits. Undoubtedly, it was a breakthrough in the field of computer vision. But with the rise of other classification methods, it fell out fashion. That was until 2012, when Krizhevsky et al. revived the interest in CNNs by exhibiting considerably higher image classification accuracy on the ImageNet challenge. Since then, the complexity of the architectures are exponentially increasing and many structures are rapidly becoming obsolete. Using multistream networks as a base and the feature infusion precept, we explore the proposed LCNN cross-fusion strategy to use the backbones of former state-of-the-art networks on image classification in order to discover if the technique is able to put these designs back in the game. In this paper, we showed that we can obtain an increase of accuracy up to

10:32 An Efficient Epileptic Seizure Detection Technique using Discrete Wavelet Transform and Machine Learning Classifiers. (arXiv:2109.13811v1 [eess.SP])

This paper presents an epilepsy detection method based on discrete wavelet transform (DWT) and Machine learning classifiers. Here DWT has been used for feature extraction as it provides a better decomposition of the signals in different frequency bands. At first, DWT has been applied to the EEG signal to extract the detail and approximate coefficients or different sub-bands. After the extraction of the coefficients, principal component analysis (PCA) has been applied on different sub-bands and then a feature level fusion technique is used to extract the important features in low dimensional feature space. Three classifiers namely: Support Vector Machine (SVM) classifier, K-Nearest-Neighbor (KNN) classifier, and Naive Bayes (NB) Classifiers have been used in the proposed work for classifying the EEG signals. The proposed method is tested on Bonn databases and provides a maximum of 100% recognition accuracy for KNN, SVM, NB classifiers.

28.09.2021
17:07 Cold nuclear fusion in lithium compound alloy

The experiments were performed in vacuum chamber in order precise measurements to be achieved and due to the relatively low concentrations of the interacting gases the amounts of the generated helium and of the generated energy (heat) were relatively low. In fact D2 gas in environment of H/H2 gas in the chamber was directed to a specimen of lithium compound alloy placed on sample holder and significant generation of both 3He and 4He was observed in all experiments as it was supported by the following facts: i) Mass analysis shows relatively high amount of 3He; ii) Mass analysis shows relatively high amount of 4He/D2 and relatively significant amount of 4HeH that confirms relatively high amount of 4He; iii) Mass analysis shows absence of H3+ (stable positive ion of three hydrogen molecule), DH (deuterium hydride) and D2H+ (stable positive ion of deuterium-deuterium hydride molecule); and iv) DC plasma spectroscopy shows peaks typical for both 3He and 4He. Based on the

11:00 Funding boost will help deliver low-carbon fusion energy

Bristol researchers will lead one of six new projects looking at novel ways to reduce the UK’s greenhouse

05:46 Learning Transport Processes with Machine Intelligence. (arXiv:2109.13096v1 [physics.plasm-ph])

We present a machine learning based approach to address the study of transport processes, ubiquitous in continuous mechanics, with particular attention to those phenomena ruled by complex micro-physics, impractical to theoretical investigation, yet exhibiting emergent behavior describable by a closed mathematical expression. Our machine learning model, built using simple components and following a few well established practices, is capable of learning latent representations of the transport process substantially closer to the ground truth than expected from the nominal error characterising the data, leading to sound generalisation properties. This is demonstrated through an idealized study of the long standing problem of heat flux suppression under conditions relevant for fusion and cosmic plasmas. A simple analysis shows that the result applies beyond those case specific assumptions and that, in particular, the accuracy of the learned representation is controllable through knowledge

05:46 Neural network tokamak equilibria with incompressible flows. (arXiv:2109.12850v1 [physics.plasm-ph])

We present several numerical results concerning the solution of a Generalized Grad-Shafranov Equation (GGSE), which governs axisymmetric plasma equilibria with incompressible flows of arbitrary direction, using fully connected, feed-forward deep neural networks, also known as multi-layer perceptrons. Solutions to the GGSE in a Tokamak-relevant D-Shaped domain are approximated by such artificial neural networks (ANNs) upon minimizing the GGSE mean squared residual in the plasma volume and the poloidal flux function on the plasma boundary. Solutions for the Solovev and the general linearizing ansatz for the free functions involved in the GGSE are obtained and benchmarked against known analytic solutions. We also construct a non-linear equilibrium incorporating characteristics relevant to the H-mode confinement. In our numerical experiments it was observed that changing the radial distribution of the training points had no appreciable effect on the accuracy of the trained solution. In

27.09.2021
08:27 Multiscale study of high energy attosecond pulse interaction with matter and application to proton-Boron fusion. (arXiv:2109.12017v1 [physics.plasm-ph])

For several decades, the interest of the scientific community in aneutronic fusion reactions such as proton-Boron fusion has grown because of potential applications in different fields. Recently, many scientific teams in the world have worked experimentally on the possibility to trigger proton-Boron fusion using intense lasers demonstrating an important renewal of interest of this field. It is now possible to generate ultra-short high intensity laser pulses at high repetition rate. These pulses also have unique properties that can be leveraged to produce proton-Boron fusion reactions. In this article, we investigate the interaction of a high energy attosecond pulse with a solid proton-Boron target and the associated ion acceleration supported by numerical simulations. We demonstrate the efficiency of single-cycle attosecond pulses in comparison to multi-cycle attosecond pulses in ion acceleration and magnetic field generation. Using these results we also propose a path to proton-Boron

24.09.2021
09:41 Hierarchical approach for energetic particle transport in 1-dimensional uniform plasmas. (arXiv:2109.11254v1 [physics.plasm-ph])

The importance of the beam-plasma system in fusion physics relies on its capability in reproducing relevant features of energetic particles interacting with the Alfv\'enic spectrum. We analyze here a multi-level hierarchy of the Vlasov-Poisson induced transport in order to characterize the underlying physical processes.

09:41 Beam model of Doppler backscattering. (arXiv:2109.10973v1 [physics.plasm-ph])

We use beam tracing -- implemented with a newly-written code, Scotty -- and the reciprocity theorem to derive a model for the linear backscattered power of the Doppler Backscattering (DBS) diagnostic. Our model works for both the O-mode and X-mode in tokamak geometry (and certain regimes of stellarators). We present the analytical derivation of our model and its implications on the DBS signal localisation and the wavenumber resolution. To determine these two quantities, we find that it is the curvature of the field lines that is important, rather than the curvature of the cut-off surface. We proceed to shed light on the hitherto poorly-understood quantitative effect of the mismatch angle. Consequently, one can use this model to correct for the attenuation due to mismatch, avoiding the need for empirical optimisation. This is especially important in spherical tokamaks, since the magnetic pitch angle is large and varies both spatially and temporally.

23.09.2021
18:53 Researchers simulate compact fusion power plant concept

Fusion power plants use magnetic fields to hold a ball of current-carrying gas (called a plasma). This creates a miniature sun that generates energy through nuclear fusion. The Compact Advanced Tokamak (CAT) concept uses state-of-the-art physics models to potentially improve fusion energy production. The models show that by carefully shaping the plasma and the distribution of current in the plasma, fusion plant operators can suppress turbulent eddies in the plasma. These eddies can cause heat loss. This will enable operators to achieve higher pressures and fusion power with lower current. This advance could help achieve a state where the plasma sustains itself and drives most of its own current.

05:42 Hierarchical Multimodal Transformer to Summarize Videos. (arXiv:2109.10559v1 [cs.CV])

Although video summarization has achieved tremendous success benefiting from Recurrent Neural Networks (RNN), RNN-based methods neglect the global dependencies and multi-hop relationships among video frames, which limits the performance. Transformer is an effective model to deal with this problem, and surpasses RNN-based methods in several sequence modeling tasks, such as machine translation, video captioning, \emph{etc}. Motivated by the great success of transformer and the natural structure of video (frame-shot-video), a hierarchical transformer is developed for video summarization, which can capture the dependencies among frame and shots, and summarize the video by exploiting the scene information formed by shots. Furthermore, we argue that both the audio and visual information are essential for the video summarization task. To integrate the two kinds of information, they are encoded in a two-stream scheme, and a multimodal fusion mechanism is developed based on the hierarchical

05:42 AI in Osteoporosis. (arXiv:2109.10478v1 [cs.CV])

In this chapter we explore and evaluate methods for trabecular bone characterization and osteoporosis diagnosis with increased interest in sparse approximations. We first describe texture representation and classification techniques, patch-based methods such as Bag of Keypoints, and more recent deep neural networks. Then we introduce the concept of sparse representations for pattern recognition and we detail integrative sparse analysis methods and classifier decision fusion methods. We report cross-validation results on osteoporosis datasets of bone radiographs and compare the results produced by the different categories of methods. We conclude that advances in the AI and machine learning fields have enabled the development of methods that can be used as diagnostic tools in clinical settings.

22.09.2021
08:32 MESSFN : a Multi-level and Enhanced Spectral-Spatial Fusion Network for Pan-sharpening. (arXiv:2109.09937v1 [cs.CV])

Dominant pan-sharpening frameworks simply concatenate the MS stream and the PAN stream once at a specific level. This way of fusion neglects the multi-level spectral-spatial correlation between the two streams, which is vital to improving the fusion performance. In consideration of this, we propose a Multi-level and Enhanced Spectral-Spatial Fusion Network (MESSFN) with the following innovations: First, to fully exploit and strengthen the above correlation, a Hierarchical Multi-level Fusion Architecture (HMFA) is carefully designed. A novel Spectral-Spatial (SS) stream is established to hierarchically derive and fuse the multi-level prior spectral and spatial expertise from the MS stream and the PAN stream. This helps the SS stream master a joint spectral-spatial representation in the hierarchical network for better modeling the fusion relationship. Second, to provide superior expertise, consequently, based on the intrinsic characteristics of the MS image and the PAN image, two feature

07:59 Verification and validation of gyrokinetic and kinetic-MHD simulations for internal kink instability in DIII-D tokamak. (arXiv:2109.09891v1 [physics.plasm-ph])

Verification and validation of the internal kink instability in tokamak have been performed for both gyrokinetic (GTC) and kinetic-MHD codes (GAM-solver, M3D-C1-K, NOVA, XTOR-K). Using realistic magnetic geometry and plasma profiles from the same equilibrium reconstruction of the DIII-D shot #141216, these codes exhibit excellent agreement for the growth rate and mode structure of the internal kink mode when all kinetic effects are suppresed. The simulated radial mode structures agree quantitatively with the electron cyclotron emission measurement after adjusting, within the experimental uncertainty, the safety factor q=1 flux-surface location in the equilibrium reconstruction. Compressible magnetic perturbations strongly destabilize the kink, while poloidal variations of the equilibrium current density reduce the growth rate of the kink. Furthermore, kinetic effects of thermal ions are found to decrease the kink growth rate in kinetic-MHD simulations, but increase the kink growth rate

21.09.2021
11:05 Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks. (arXiv:2109.08956v1 [physics.plasm-ph])

Next generation high performance (HP) tokamaks risk damage from unmitigated disruptions at high current and power. Achieving reliable disruption prediction for a device's HP operation based on its low performance (LP) data is key to success. In this letter, through explorative data analysis and dedicated numerical experiments on multiple existing tokamaks, we demonstrate how the operational regimes of tokamaks can affect the power of a trained disruption predictor. First, our results suggest data-driven disruption predictors trained on abundant LP discharges work poorly on the HP regime of the same tokamak, which is a consequence of the distinct distributions of the tightly correlated signals related to disruptions in these two regimes. Second, we find that matching operational parameters among tokamaks strongly improves cross-machine accuracy which implies our model learns from the underlying scalings of dimensionless physics parameters like q_{95}, \beta_{p} and confirms the

10:31 Impact of Surface and Pore Characteristics on Fatigue Life of Laser Powder Bed Fusion Ti-6Al-4V Alloy Described by Neural Network Models. (arXiv:2109.09655v1 [cond-mat.mtrl-sci])

In this study, the effects of surface roughness and pore characteristics on fatigue lives of laser powder bed fusion (LPBF) Ti-6Al-4V parts were investigated. The 197 fatigue bars were printed using the same laser power but with varied scanning speeds. These actions led to variations in the geometries of microscale pores, and such variations were characterized using micro-computed tomography. To generate differences in surface roughness in fatigue bars, half of the samples were grit-blasted and the other half machined. Fatigue behaviors were analyzed with respect to surface roughness and statistics of the pores. For the grit-blasted samples, the contour laser scan in the LPBF strategy led to a pore-depletion zone isolating surface and internal pores with different features. For the machined samples, where surface pores resemble internal pores, the fatigue life was highly correlated with the average pore size and projected pore area in the plane perpendicular to the stress direction.

10:31 Plasma screening of nuclear fusion reactions in liquid layers of compact degenerate stars: a first-principle study. (arXiv:2109.09445v1 [astro-ph.HE])

A reliable description of nuclear fusion reactions in inner layers of white dwarfs and envelopes of neutron stars is important for realistic modelling of a wide range of observable astrophysical phenomena from accreting neutron stars to type Ia supernovae. We study the problem of screening of the Coulomb barrier impeding the reactions, by a plasma surrounding the fusing nuclei. Numerical calculations of the screening factor are performed from the first principles with the aid of quantum-mechanical path integrals in the model of a one-component plasma of atomic nuclei for temperatures and densities typical for dense liquid layers of compact degenerate stars. We do not rely on various quasiclassic approximations widely used in the literature, such as factoring-out the tunneling process, tunneling in an average spherically symmetric mean-force potential, usage of classic free energies and pair correlation functions, linear mixing rule and so on. In general, a good agreement with earlier

10:31 Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks. (arXiv:2109.08956v1 [physics.plasm-ph])

Next generation high performance (HP) tokamaks risk damage from unmitigated disruptions at high current and power. Achieving reliable disruption prediction for a device's HP operation based on its low performance (LP) data is key to success. In this letter, through explorative data analysis and dedicated numerical experiments on multiple existing tokamaks, we demonstrate how the operational regimes of tokamaks can affect the power of a trained disruption predictor. First, our results suggest data-driven disruption predictors trained on abundant LP discharges work poorly on the HP regime of the same tokamak, which is a consequence of the distinct distributions of the tightly correlated signals related to disruptions in these two regimes. Second, we find that matching operational parameters among tokamaks strongly improves cross-machine accuracy which implies our model learns from the underlying scalings of dimensionless physics parameters like q_{95}, \beta_{p} and confirms the

10:31 Fusion Driven Transmutation of Transuranics in a Molten Salt. (arXiv:2109.08741v1 [nucl-th])

A first set of computational studies of transmutation of spent nuclear fuel using compact tunable 14 MeV D-T fusion driven neutron sources is presenter. Where we study the controllability, time evolution, as well as effects of spatial distribution of the neutronics in the transmutation in the subcritical operations regime of a transmutator, in which our neutron sources are small, distributed, and can be monitored. Source neutrons are generated via beam-target fusion whereas a deuteron beam is created by laser irradiation of nanometric foils, through the Coherent Acceleration of Ions by Laser (CAIL) process, onto a tritium soaked target. This can be accomplished using relatively cheap fiber lasers terminating onto small scale targets which makes possible the use of multiple tunable and distributable neutron sources. This source is then combined with a molten salt core whose liquid state allows: homogeneity by mixing, safety, in-situ processing, and monitoring. Such a source and molten

20.09.2021
17:21 The Race for Fusion Power Heats Up as Multiple Projects Hit New Milestones

Fusion power could be a silver bullet for the world’s energy and environmental woes, but it’s famously always 30 years away. A recent flurry of announcements are raising hopes that maybe the timeline has started to tighten. The technology has huge potential because it promises to generate enormous amounts of energy from abundant fuel that […]

06:09 Experimental Evidence of Nonlinear Avalanche Dynamics of Energetic Particle Modes. (arXiv:2109.08545v1 [physics.plasm-ph])

Recent observations in HL-2A tokamak give new experimental evidences of energetic particle mode (EPM) avalanche. In a strong EPM burst, the mode structure propagates radially outward within two hundred Alfv\'en time, while the frequency of the dominant mode changes self-consistently to maximize wave-particle power exchange and mode growth. This suggests that significant energetic particle transport occurs in this avalanche phase, in agreement with theoretical framework of EPM convective amplification. A simplified relay runner model yields satisfactory interpretations of the measurements. The results can help understanding the nonlinear dynamics of energetic particle driven modes in future burning plasmas, such as ITER.

17.09.2021
07:42 Probability-driven scoring functions in combining linear classifiers. (arXiv:2109.07815v1 [cs.LG])

Although linear classifiers are one of the oldest methods in machine learning, they are still very popular in the machine learning community. This is due to their low computational complexity and robustness to overfitting. Consequently, linear classifiers are often used as base classifiers of multiple ensemble classification systems. This research is aimed at building a new fusion method dedicated to the ensemble of linear classifiers. The fusion scheme uses both measurement space and geometrical space. Namely, we proposed a probability-driven scoring function which shape depends on the orientation of the decision hyperplanes generated by the base classifiers. The proposed fusion method is compared with the reference method using multiple benchmark datasets taken from the KEEL repository. The comparison is done using multiple quality criteria. The statistical analysis of the obtained results is also performed. The experimental study shows that, under certain conditions, some

07:42 Single-camera Two-Wavelength Imaging Pyrometry for Melt Pool Temperature Measurement and Monitoring in Laser Powder Bed Fusion based Additive Manufacturing. (arXiv:2109.07472v1 [eess.SY])

Melt pool (MP) temperature is one of the determining factors and key signatures for the properties of printed components during metal additive manufacturing (AM). The state-of-the art measurement systems are hindered by both the equipment cost and the large-scale data acquisition and processing demands. In this work, we introduce a novel coaxial high-speed single-camera two-wavelength imaging pyrometer (STWIP) system as opposed to the typical utilization of multiple cameras for measuring MP temperature profiles through a laser powder bed fusion (LPBF) process. Developed on a commercial LPBF machine (EOS M290), the STWIP system is demonstrated to be able to quantitatively monitor MP temperature and variation for 50 layers at high framerates (> 30,000 fps) during a print of five standard fatigue specimens. High performance computing is employed to analyze the acquired big data of MP images for determining each MPs average temperature and 2D temperature profile. The MP temperature

06:37 Single-camera Two-Wavelength Imaging Pyrometry for Melt Pool Temperature Measurement and Monitoring in Laser Powder Bed Fusion based Additive Manufacturing. (arXiv:2109.07472v1 [eess.SY])

Melt pool (MP) temperature is one of the determining factors and key signatures for the properties of printed components during metal additive manufacturing (AM). The state-of-the art measurement systems are hindered by both the equipment cost and the large-scale data acquisition and processing demands. In this work, we introduce a novel coaxial high-speed single-camera two-wavelength imaging pyrometer (STWIP) system as opposed to the typical utilization of multiple cameras for measuring MP temperature profiles through a laser powder bed fusion (LPBF) process. Developed on a commercial LPBF machine (EOS M290), the STWIP system is demonstrated to be able to quantitatively monitor MP temperature and variation for 50 layers at high framerates (> 30,000 fps) during a print of five standard fatigue specimens. High performance computing is employed to analyze the acquired big data of MP images for determining each MPs average temperature and 2D temperature profile. The MP temperature

15.09.2021
04:38 Scientists explore the creation of artificial organelles

Scientists explore the creation of artificial organelles. Artificial organelles generated from Exosome fusion can function as energy reserves in the damaged tissues.

13.09.2021
10:56 Hybrid modeling of the human cardiovascular system using NeuralFMUs. (arXiv:2109.04880v1 [cs.LG])

Hybrid modeling, the combination of first principle and machine learning models, is an emerging research field that gathers more and more attention. Even if hybrid models produce formidable results for academic examples, there are still different technical challenges that hinder the use of hybrid modeling in real-world applications. By presenting NeuralFMUs, the fusion of a FMU, a numerical ODE solver and an ANN, we are paving the way for the use of a variety of first principle models from different modeling tools as parts of hybrid models. This contribution handles the hybrid modeling of a complex, real-world example: Starting with a simplified 1D-fluid model of the human cardiovascular system (arterial side), the aim is to learn neglected physical effects like arterial elasticity from data. We will show that the hybrid modeling process is more comfortable, needs less system knowledge and is therefore less error-prone compared to modeling solely based on first principle. Further, the

10.09.2021
15:04 'This is not hype, this is reality': Nuclear fusion gets a step closer to reality as scientists successfully test a magnet 12 times as powerful as those used in MRIs - with a working reactor slated within the next decade

MIT researchers created a new high temperature superconducting magnet This allows for it to be smaller than magnets used in other fusion experiments

04:22 Investigation of the effectiveness of non-inductive `multi-harmonic' electron cyclotron current drive through modeling multi-pass absorptions in the EXL-50 spherical tokamak. (arXiv:2109.04161v1 [physics.plasm-ph])

The effectiveness of multiple electron cyclotron resonance (ECR) harmonics has been thoroughly investigated in context of high current drive efficiency, generally observed in fully non-inductive operation of the low aspect ratio EXL-50 spherical tokamak (ST) powered by electron cyclotron (EC) waves. The Fokker-Plank equation is numerically solved to obtain electron distribution function, under steady state of the relativistic nonlinear Coulomb collision and quasi-linear diffusion operators, for calculating plasma current driven by the injected EC wave. For the extra-ordinary EC wave, simulation results unfold a mechanism by which electrons moving around the cold second harmonic ECR layer strongly resonate with higher harmonics via the relativistic Doppler shifted resonance condition. This feature is in fact evident above a certain value of input EC wave power in simulation, indicating it to be a non-linear phenomenon. Similar to the experimental observation, high efficiency in current

09.09.2021
12:34 Magnet milestones move distant nuclear fusion dream closer

Teams working on two continents have marked similar milestones in their respective efforts to tap an energy source key to the fight against climate change: They've each produced very impressive magnets.

07:05 Retention of Fast Alpha Particles and Expulsion of Helium Ash by an Internal Disruption in a Tokamak Plasma. (arXiv:2109.03427v1 [physics.plasm-ph])

An internal disruption is simulated in a large tokamak plasma with monotonic safety factor profile close to unity. The domain and the time scale of the event are set to match observations. The simulation follows passive alpha particles with energies 35 keV-3.5 MeV, whose initial density peak is localized in the disrupting domain. While the 35 keV profile flattens, a synergy of multiple physical factors allows the 3.5 MeV profile to remain peaked, motivating the use of moderate internal disruptions in a fusion reactor to expel helium ash while preserving good confinement of fast alphas.

08.09.2021
23:05 Fusion gets closer with successful test of new kind of magnet at MIT start-up backed by Bill Gates

If fusion can be achieved on earth and commercialized, it will provide a nearly unlimited source of clean energy without producing the waste of nuclear fission.

23:05 Fusion gets closer with successful test of new kind of magnet at MIT start-up backed by Bill Gates

If fusion can be achieved on earth and commercialized, it will provide a nearly unlimited source of clean energy without producing the waste of nuclear fission.

14:46 New superconducting magnet breaks magnetic field strength records, paving the way for fusion energy

It was a moment three years in the making, based on intensive research and design work: On Sept. 5, for the first time, a large high-temperature superconducting electromagnet was ramped up to a field strength of 20 tesla, the most powerful magnetic field of its kind ever created on Earth. That successful demonstration helps resolve the greatest uncertainty in the quest to build the world's first fusion power plant that can produce more power than it consumes, according to the project's leaders at MIT and startup company Commonwealth Fusion Systems (CFS).

07.09.2021
06:15 MONITOR: A Multimodal Fusion Framework to Assess Message Veracity in Social Networks. (arXiv:2109.02271v1 [cs.SI])

Users of social networks tend to post and share content with little restraint. Hence, rumors and fake news can quickly spread on a huge scale. This may pose a threat to the credibility of social media and can cause serious consequences in real life. Therefore, the task of rumor detection and verification has become extremely important. Assessing the veracity of a social media message (e.g., by fact checkers) involves analyzing the text of the message, its context and any multimedia attachment. This is a very time-consuming task that can be much helped by machine learning. In the literature, most message veracity verification methods only exploit textual contents and metadata. Very few take both textual and visual contents, and more particularly images, into account. In this paper, we second the hypothesis that exploiting all of the components of a social media post enhances the accuracy of veracity detection. To further the state of the art, we first propose using a set of advanced

06:15 F3S: Free Flow Fever Screening. (arXiv:2109.01733v1 [cs.CV])

Identification of people with elevated body temperature can reduce or dramatically slow down the spread of infectious diseases like COVID-19. We present a novel fever-screening system, F3S, that uses edge machine learning techniques to accurately measure core body temperatures of multiple individuals in a free-flow setting. F3S performs real-time sensor fusion of visual camera with thermal camera data streams to detect elevated body temperature, and it has several unique features: (a) visual and thermal streams represent very different modalities, and we dynamically associate semantically-equivalent regions across visual and thermal frames by using a new, dynamic alignment technique that analyzes content and context in real-time, (b) we track people through occlusions, identify the eye (inner canthus), forehead, face and head regions where possible, and provide an accurate temperature reading by using a prioritized refinement algorithm, and (c) we robustly detect elevated body

06.09.2021
08:27 Validation of edge turbulence codes against the TCV-X21 diverted L-mode reference case. (arXiv:2109.01618v1 [physics.plasm-ph])

Full-size turbulence simulations of the divertor and scrape-off-layer of existing tokamaks have recently become feasible, allowing direct comparisons of turbulence simulations to experimental measurements. We present a validation of three flux-driven turbulence codes (GBS, GRILLIX and TOKAM3X) against an experimental dataset from diverted Ohmic L-mode discharges on the TCV tokamak. The dataset covers the divertor targets, volume, entrance and OMP via 5 diagnostic systems, giving a total of 45 comparison observables over two toroidal field directions. The simulations show good agreement at the OMP for most observables. At the targets and in the divertor volume, several observables show good agreement, but the overall match is lower than at the OMP. The simulations typically find the correct order-of-magnitude and the approximate shape for the divertor mean profiles. The experimental profiles of the divertor density, potential, current and velocity vary strongly with field direction,

03.09.2021
10:09 Wearable-based Classification of Running Styles with Deep Learning. (arXiv:2109.00594v1 [cs.LG])

Automatic classification of running styles can enable runners to obtain feedback with the aim of optimizing performance in terms of minimizing energy expenditure, fatigue, and risk of injury. To develop a system capable of classifying running styles using wearables, we collect a dataset from 10 healthy runners performing 8 different pre-defined running styles. Five wearable devices are used to record accelerometer data from different parts of the lower body, namely left and right foot, left and right medial tibia, and lower back. Using the collected dataset, we develop a deep learning solution which consists of a Convolutional Neural Network and Long Short-Term Memory network to first automatically extract effective features, followed by learning temporal relationships. Score-level fusion is used to aggregate the classification results from the different sensors. Experiments show that the proposed model is capable of automatically classifying different running styles in a

09:14 Positive energy representations of affine algebras and Stokes matrices of the affine Toda equations. (arXiv:2109.00765v1 [math.DG])

We give a construction which produces a positive energy representation of the affine Lie algebra of type A_n from the Stokes data of a solution of the tt*-Toda equations of type A_n. The construction appears to play a role in conformal field theory. We illustrate this with several examples: the fusion ring, W-algebra minimal models (Argyres-Douglas theory), as well as topological-antitopological fusion itself.

09:14 The available energy of trapped electrons and its relation to turbulent transport. (arXiv:2109.01042v1 [physics.plasm-ph])

Any magnetically confined plasma possesses a certain amount of "available energy", defined as that part of the thermal energy that can be converted into instabilities and turbulence. Here, we present a calculation of the available energy carried by magnetically trapped electrons in a slender flux tube of collisionless plasma. This quantity is compared with nonlinear gyrokinetic simulations of the turbulent energy flux resulting from collisionless turbulence driven by a density gradient in various tokamak and stellarator devices. The numerical calculation of available energy is extremely fast and shows a strong correlation with energy fluxes found in the gyrokinetic simulations, which can be expressed as a simple power law and understood in terms of a phenomenological model.

09:14 Simulation of convective transport during frequency chirping of a TAE using the MEGA code. (arXiv:2109.01032v1 [physics.plasm-ph])

We present a procedure to examine energetic particle phase-space during long range frequency chirping phenomena in tokamak plasmas. To apply the proposed method, we have performed self-consistent simulations using the MEGA code and analyzed the simulation data. We demonstrate a travelling wave in phase-space and that there exist specific slices of phase-space on which the resonant particles lie throughout the wave evolution. For non-linear evolution of an n=6 toroidicity-induced Alfven eigenmode (TAE), our results reveal the formation of coherent phase-space structures (holes/clumps) after coarse-graining of the distribution function. These structures cause a convective transport in phase-space which implies a radial drift of the resonant particles. We also demonstrate that the rate of frequency chirping increases with the TAE damping rate. Our observations of the TAE behaviour and the corresponding phase-space dynamics are consistent with the Berk-Breizman (BB) theory.

02.09.2021
18:27 Negative triangularity—a positive for tokamak fusion reactors

Tokamak devices use strong magnetic fields to confine and to shape the plasma that contains the fuel that achieves fusion. The shape of the plasma affects the ease or difficulty of achieving a viable fusion power source. In a conventional tokamak, the cross-section of the plasma is shaped like the capital letter D. When the straight part of the D faces the "donut hole" side of the donut-shaped tokamak, this shape is called positive triangularity. When the plasma cross-section is in a backwards D shape and the curved part of the D faces the "donut hole" side, then this shape is called negative triangularity. New research shows that negative triangularity reduces how much the plasma interacts with the plasma-facing material surfaces of the tokamak. This finding points to critical benefits for achieving nuclear fusion power.

15:54 How to calculate the ideal ingredients for nuclear fusion with the most energy

Nuclear fusion is regarded as the energy of the future. It does not emit CO2, it is safe and it provides a lot of energy that can easily supply large cities with electricity. Nuclear fusion is very interesting in theory, but not yet in practice. Scientists have already succeeded in making nuclear fusion happen, but to make it profitable a lot of research still needs to take place in the coming years. TU/e researcher Michele Marin takes his part with his research on nuclear fusion plasma.

01.09.2021
08:35 Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion. (arXiv:2108.13577v1 [cs.LG])

Brain strain and strain rate are effective in predicting traumatic brain injury (TBI) caused by head impacts. However, state-of-the-art finite element modeling (FEM) demands considerable computational time in the computation, limiting its application in real-time TBI risk monitoring. To accelerate, machine learning head models (MLHMs) were developed, and the model accuracy was found to decrease when the training/test datasets were from different head impacts types. However, the size of dataset for specific impact types may not be enough for model training. To address the computational cost of FEM, the limited strain rate prediction, and the generalizability of MLHMs to on-field datasets, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR). We trained and tested the MLHMs on 13,623 head impacts from simulations, American football, mixed martial arts, car crash, and compared

07:30 Virtual screening of Microalgal compounds as potential inhibitors of Type 2 Human Transmembrane serine protease (TMPRSS2). (arXiv:2108.13764v1 [q-bio.BM])

More than 198 million cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been reported that result in no fewer than 4.2 million deaths globally. The rapid spread of the disease coupled with the lack of specific registered drugs for its treatment pose a great challenge that necessitate the development of therapeutic agents from a variety of sources. In this study, we employed an in-silico method to screen natural compounds with a view to identify inhibitors of the human transmembrane protease serine type 2 (TMPRSS2). The activity of this enzyme is essential for viral access into the host cells via angiotensin-converting enzyme 2 (ACE-2). Inhibiting the activity of this enzyme is therefore highly crucial for preventing viral fusion with ACE-2 thus shielding SARS-CoV-2 infectivity. 3D model of TMPRSS2 was constructed using I-TASSER, refined by GalaxyRefine, validated by Ramachandran plot server and overall model quality was checked by ProSA. 95 natural compounds

07:30 Single-molecular quantification of flowering control proteins within nuclear condensates in live whole Arabidopsis root. (arXiv:2108.13743v1 [q-bio.BM])

Here we describe the coupled standardisation of two complementary fluorescence imaging techniques and apply it to liquid-liquid phase separated condensates formed from an EGFP fluorescent reporter of Flowering Control Locus A (FCA), a protein that associates with chromosomal DNA in plants during epigenetic regulation of the flowering process. First, we use home-built single-molecule Slimfield microscopy to establish a fluorescent protein standard. This sample comprises live yeast cells expressing Mig1 protein, a metabolic regulator which localises to the nucleus under conditions of high glucose, fused to the same type of EGFP label as for the FCA fusion construct. Then we employ commercial confocal AiryScan microscopy to study the same standard. Finally, we demonstrate how to quantify FCA-EGFP nuclear condensates in intact root tips at rapid timescales and apply this calibration. This method is a valuable approach to obtaining single-molecule precise stoichiometry and copy number

07:30 Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion. (arXiv:2108.13577v1 [cs.LG])

Brain strain and strain rate are effective in predicting traumatic brain injury (TBI) caused by head impacts. However, state-of-the-art finite element modeling (FEM) demands considerable computational time in the computation, limiting its application in real-time TBI risk monitoring. To accelerate, machine learning head models (MLHMs) were developed, and the model accuracy was found to decrease when the training/test datasets were from different head impacts types. However, the size of dataset for specific impact types may not be enough for model training. To address the computational cost of FEM, the limited strain rate prediction, and the generalizability of MLHMs to on-field datasets, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR). We trained and tested the MLHMs on 13,623 head impacts from simulations, American football, mixed martial arts, car crash, and compared

07:30 Single-molecular quantification of flowering control proteins within nuclear condensates in live whole Arabidopsis root. (arXiv:2108.13743v1 [q-bio.BM])

Here we describe the coupled standardisation of two complementary fluorescence imaging techniques and apply it to liquid-liquid phase separated condensates formed from an EGFP fluorescent reporter of Flowering Control Locus A (FCA), a protein that associates with chromosomal DNA in plants during epigenetic regulation of the flowering process. First, we use home-built single-molecule Slimfield microscopy to establish a fluorescent protein standard. This sample comprises live yeast cells expressing Mig1 protein, a metabolic regulator which localises to the nucleus under conditions of high glucose, fused to the same type of EGFP label as for the FCA fusion construct. Then we employ commercial confocal AiryScan microscopy to study the same standard. Finally, we demonstrate how to quantify FCA-EGFP nuclear condensates in intact root tips at rapid timescales and apply this calibration. This method is a valuable approach to obtaining single-molecule precise stoichiometry and copy number

07:30 Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion. (arXiv:2108.13577v1 [cs.LG])

Brain strain and strain rate are effective in predicting traumatic brain injury (TBI) caused by head impacts. However, state-of-the-art finite element modeling (FEM) demands considerable computational time in the computation, limiting its application in real-time TBI risk monitoring. To accelerate, machine learning head models (MLHMs) were developed, and the model accuracy was found to decrease when the training/test datasets were from different head impacts types. However, the size of dataset for specific impact types may not be enough for model training. To address the computational cost of FEM, the limited strain rate prediction, and the generalizability of MLHMs to on-field datasets, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR). We trained and tested the MLHMs on 13,623 head impacts from simulations, American football, mixed martial arts, car crash, and compared

07:29 Observation and modelling of Stimulated Raman Scattering driven by an optically smoothed laser beam in experimental conditions relevant for Shock Ignition. (arXiv:2108.13485v1 [physics.plasm-ph])

We report results and modelling of an experiment performed at the TAW Vulcan laser facility, aimed at investigating laser-plasma interaction in conditions which are of interest for the Shock Ignition scheme to Inertial Confinement Fusion, i.e. laser intensity higher than 10^16 W/cm2 impinging on a hot (T > 1 keV), inhomogeneous and long scalelength preformed plasma. Measurements show a significant SRS backscattering (4 - 20% of laser energy) driven at low plasma densities and no signatures of TPD/SRS driven at the quarter critical density region. Results are satisfactorily reproduced by an analytical model accounting for the convective SRS growth in independent laser speckles, in conditions where the reflectivity is dominated by the contribution from the most intense speckles, where SRS gets saturated. Analytical and kinetic simulations well reproduce the onset of SRS at low plasma densities in a regime strongly affected by non linear Landau damping and by filamentation of the most

31.08.2021
22:23 Speculating on Involvement of SIRT1 and SIRT3 in the Aging of the Heart

In today's research materials, scientists demonstrate that the combination of reduced SIRT1 and SIRT3 causes weakness in heart muscle via disruption of mitochondrial function. Mitochondria are the power plants of the cell, a herd of hundreds of bacteria-like organelles that are responsible for producing the chemical energy store molecule adenosine triphosphate (ATP) to power cellular operations. Impairment of mitochondrial function thus results in impaired cell function, a characteristic change observed in old tissues. Mitochondrial dynamics, the balance of fusion and fission of mitochondria, shift with age in ways that impair the processes of mitophagy that are responsible for removing damaged mitochondria. This leads to impaired function as damaged mitochondrial accumulate. A number of lines of evidence suggest that improved mitophagy can help restore mitochondrial function […]

17:48 Physicist helps confirm a major advance in stellarator performance

Results of a heat-confinement experiment on the twisty Wendelstein 7-X stellarator in Germany could enable devices based on the W7-X design to lead to a practical fusion reactor.

17:29 State-of-the-art computer code could advance efforts to harness fusion energy

Think of light bulb filaments that glow when you flip a switch. That glow also occurs in magnetic fusion facilities known as tokamaks that are designed to harness the energy that powers the sun and stars. Understanding how resistivity, the process that produces the glow, affects these devices could help scientists design them to operate more efficiently.

11:22 Multimodal Data Fusion in High-Dimensional Heterogeneous Datasets via Generative Models. (arXiv:2108.12445v1 [cs.LG])

The commonly used latent space embedding techniques, such as Principal Component Analysis, Factor Analysis, and manifold learning techniques, are typically used for learning effective representations of homogeneous data. However, they do not readily extend to heterogeneous data that are a combination of numerical and categorical variables, e.g., arising from linked GPS and text data. In this paper, we are interested in learning probabilistic generative models from high-dimensional heterogeneous data in an unsupervised fashion. The learned generative model provides latent unified representations that capture the factors common to the multiple dimensions of the data, and thus enable fusing multimodal data for various machine learning tasks. Following a Bayesian approach, we propose a general framework that combines disparate data types through the natural parameterization of the exponential family of distributions. To scale the model inference to millions of instances with thousands of

10:16 Multimodal Data Fusion in High-Dimensional Heterogeneous Datasets via Generative Models. (arXiv:2108.12445v1 [cs.LG])

The commonly used latent space embedding techniques, such as Principal Component Analysis, Factor Analysis, and manifold learning techniques, are typically used for learning effective representations of homogeneous data. However, they do not readily extend to heterogeneous data that are a combination of numerical and categorical variables, e.g., arising from linked GPS and text data. In this paper, we are interested in learning probabilistic generative models from high-dimensional heterogeneous data in an unsupervised fashion. The learned generative model provides latent unified representations that capture the factors common to the multiple dimensions of the data, and thus enable fusing multimodal data for various machine learning tasks. Following a Bayesian approach, we propose a general framework that combines disparate data types through the natural parameterization of the exponential family of distributions. To scale the model inference to millions of instances with thousands of

10:16 Magnetic turnstiles in nonresonant stellarator divertor. (arXiv:2108.12555v1 [physics.plasm-ph])

A very important result on the magnetic turnstiles in the nonresonant stellarator divertor [A. Punjabi and A. H. Boozer, Phys. Plasmas 27, 012503 (2020)] is obtained. The exiting and entering magnetic flux tubes of a distinct type of magnetic turnstile in the nonresonant stellarator divertor do not leave and enter the outermost confining surface at the same location but on the opposite sides of the outermost confining surface. This result is contrary to the conventional understanding of the turnstiles in Hamiltonian mechanics [R. S. Mackay, J. D. Meiss, and I. C. Percival, Physica D 13, 55 (1984); J. D. Meiss, Chaos 25, 097602 (2015)]. There are two distinct types of true magnetic turnstiles with probability exponents 9/5 and 9/4, and there is also one type of pseudo magnetic turnstile. The questions of the nature of continuous toroidal stripes and the secondary family having a negative probability exponent from our simulation of nonresonant stellarator divertor [A. Punjabi and A. H.

30.08.2021
23:24 Physicist helps confirm a major advance in stellarator performance for fusion energy

Stellarators, twisty magnetic devices that aim to harness on Earth the fusion energy that powers the sun and stars, have long played second fiddle to more widely used doughnut-shaped facilities known as tokamaks. The complex twisted stellarator magnets have been difficult to design and have previously allowed greater leakage of the superhigh heat from fusion reactions.

27.08.2021
23:25 US achieves laser-fusion record: what it means for nuclear-weapons research

06:52 Velocity space compression from Fermi acceleration with Lorentz scattering. (arXiv:2108.11913v1 [physics.plasm-ph])

The Fermi acceleration model was introduced to describe how cosmic ray particles are accelerated to great speeds by interacting with moving magnetic fields. We identify a new variation of the model where light ions interact with a moving wall while undergoing pitch angle scattering through Coulomb collisions due to the presence of a heavier ionic species. The collisions introduce a stochastic component which adds complexity to the particle acceleration profile and sets it apart from collisionless Fermi acceleration models. The unusual effect captured by this simplified variation of Fermi acceleration is the non-conservation of phase space, with the possibility for a distribution of particles initially monotonically decreasing in energy to exhibit an energy peak upon compression. A peaked energy distribution might have interesting applications, such as to optimize fusion reactivity or to characterize astrophysical phenomena that exhibit non-thermal features.

06:52 Adjoint methods for quasisymmetry of vacuum fields on a surface. (arXiv:2108.11433v1 [physics.plasm-ph])

Adjoint methods can speed up stellarator optimisation by providing gradient information more efficiently compared to finite-difference evaluations. Adjoint methods are herein applied to vacuum magnetic fields, with objective functions targeting quasisymmetry and a rotational transform value on a surface. To measure quasisymmetry, a novel way of evaluating approximate flux coordinates on a single flux surface without the assumption of a neighbourhood of flux surfaces is proposed. The shape gradients obtained from the adjoint formalism are evaluated numerically and verified against finite-difference evaluations.

26.08.2021
09:40 Linear gyrokinetic stability of a high $\beta$ non-inductive spherical tokamak. (arXiv:2108.11169v1 [physics.plasm-ph])

Spherical tokamaks (STs) have been shown to possess properties desirable for a fusion power plant such as achieving high plasma ? and having increased vertical stability. To understand the confinement properties that might be expected in the conceptual design for a high $\beta$ ST fusion reactor, a 1GW ST plasma equilibrium was analysed using local linear gyrokinetics to determine the type of micro-instabilities that arise. Kinetic ballooning modes (KBMs) and micro-tearing modes (MTMs) are found to be the dominant instabilities. The parametric dependence of these linear modes was determined and from the insights gained, the equilibrium was tuned to find a regime marginally stable to all micro-instabilities at $\theta_0$ = 0:0. This work identifies the most important micro-instabilities expected to generate turbulent transport in high $\beta$ STs. The impact of such modes must be faithfully captured in first principles based reduced models of anomalous transport that are needed for

25.08.2021
06:32 Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues. (arXiv:2108.10509v1 [cs.MM])

Recently, fake news with text and images have achieved more effective diffusion than text-only fake news, raising a severe issue of multimodal fake news detection. Current studies on this issue have made significant contributions to developing multimodal models, but they are defective in modeling the multimodal content sufficiently. Most of them only preliminarily model the basic semantics of the images as a supplement to the text, which limits their performance on detection. In this paper, we find three valuable text-image correlations in multimodal fake news: entity inconsistency, mutual enhancement, and text complementation. To effectively capture these multimodal clues, we innovatively extract visual entities (such as celebrities and landmarks) to understand the news-related high-level semantics of images, and then model the multimodal entity inconsistency and mutual enhancement with the help of visual entities. Moreover, we extract the embedded text in images as the

05:17 Discrete Boltzmann Modeling of Plasma Shock Wave. (arXiv:2108.10590v1 [physics.plasm-ph])

Plasma shock waves widely exist and play an important role in high-energy-density environment, especially in the inertial confinement fusion. Due to the large gradient of macroscopic physical quantities and the coupled thermal, electrical, magnetic and optical phenomena, there exist not only hydrodynamic non-equilibrium (HNE) effects, but also strong thermodynamic non-equilibrium (TNE) effects around the wavefront. In this work, a two-dimensional single-fluid discrete Boltzmann model is proposed to investigate the physical structure of ion shock. The electron is assumed inertialess and always in thermodynamic equilibrium. The Rankine-Hugoniot relations for single fluid theory of plasma shock wave is derived. It is found that the physical structure of shock wave in plasma is significantly different from that in normal fluid and somewhat similar to that of detonation wave from the sense that a peak appears in the front. The non-equilibrium effects around the shock front become stronger

05:17 Two-dimensional beam profile monitor for the detection of alpha-emitting radioactive isotope beam. (arXiv:2108.10556v1 [physics.ins-det])

Ions with similar charge-to-mass ratios cannot be separated from existing beam profile monitors (BPMs) in nuclear facilities in which low-energy radioactive ions are produced due to nuclear fusion reactions. In this study, we developed a BPM using a microchannel plate and a charge-coupled device to differentiate the beam profiles of alpha-decaying radioactive isotopes from other ions (reaction products) produced in a nuclear reaction. This BPM was employed to optimize the low-energy radioactive francium ion (Fr+) beam developed at the Cyclotron and Radioisotope Center (CYRIC), Tohoku University, for electron permanent electric dipole moment (e-EDM) search experiments using Fr atoms. We demonstrated the performance of the BPM by separating the Fr+ beam from other reaction products produced during the nuclear fusion reaction of an oxygen (18O) beam and gold (197Au) target. However, as the mass of Au is close to that of Fr, separating the ions of these elements using a mass filter is a

24.08.2021
09:19 Using Large Pre-Trained Models with Cross-Modal Attention for Multi-Modal Emotion Recognition. (arXiv:2108.09669v1 [eess.AS])

Recently, self-supervised pre-training has shown significant improvements in many areas of machine learning, including speech and NLP. We propose using large self-supervised pre-trained models for both audio and text modality with cross-modality attention for multimodal emotion recognition. We use Wav2Vec2.0 [1] as an audio encoder base for robust speech features extraction and the BERT model [2] as a text encoder base for better contextual representation of text. These high capacity models trained on large amounts of unlabeled data contain rich feature representations and improve the downstream task's performance. We use the cross-modal attention [3] mechanism to learn alignment between audio and text representations from self-supervised models. Cross-modal attention also helps in extracting interactive information between audio and text features. We obtain utterance-level feature representation from frame-level features using statistics pooling for both audio and text modality and

09:19 Temporally Nonstationary Component Analysis; Application to Noninvasive Fetal Electrocardiogram Extraction. (arXiv:2108.09353v1 [eess.SP])

Objective: Mixtures of temporally nonstationary signals are very common in biomedical applications. The nonstationarity of the source signals can be used as a discriminative property for signal separation. Herein, a semi-blind source separation algorithm is proposed for the extraction of temporally nonstationary components from linear multichannel mixtures of signals and noises. Methods: A hypothesis test is proposed for the detection and fusion of temporally nonstationary events, by using ad hoc indexes for monitoring the first and second order statistics of the innovation process. As proof of concept, the general framework is customized and tested over noninvasive fetal cardiac recordings acquired from the maternal abdomen, over publicly available datasets, using two types of nonstationarity detectors: 1) a local power variations detector, and 2) a model-deviations detector using the innovation process properties of an extended Kalman filter. Results: The performance of the proposed

08:02 Screening of resonant magnetic perturbation penetration by flows in tokamak plasmas based on two-fluid model. (arXiv:2108.09621v1 [physics.plasm-ph])

Numerical simulation on the resonant magnetic perturbation penetration is carried out by the newly-updated initial value code MDC (MHD@Dalian Code). Based on a set of two-fluid four-field equations, the bootstrap current, parallel and perpendicular transport effects are included appropriately. Taking into account the bootstrap current, a mode penetration like phenomenon is found, which is essentially different from the classical tearing mode model. It may provide a possible explanation for the finite mode penetration threshold at zero rotation detected in experiments. To reveal the influence of diamagnetic drift flow on the mode penetration process, $\bf E\times B$ drift flow and diamagnetic drift flow are separately applied to compare their effects. Numerical results show that, a sufficiently large diamagnetic drift flow can drive a strong stabilizing effect on the neoclassical tearing mode. Furthermore, an oscillation phenomenon of island width is discovered. By analyzing in depth,

23.08.2021
10:45 Near real-time streaming analysis of big fusion data. (arXiv:2108.08896v1 [physics.plasm-ph])

While experiments on fusion plasmas produce high-dimensional data time series with ever increasing magnitude and velocity, data analysis has been lagging behind this development. For example, many data analysis tasks are often performed in a manual, ad-hoc manner some time after an experiment. In this article we introduce the DELTA framework that facilitates near real-time streaming analysis of big and fast fusion data. By streaming measurement data from fusion experiments to a high-performance compute center, DELTA allows to perform demanding data analysis tasks in between plasma pulses. This article describe the modular and expandable software architecture of DELTA and presents performance benchmarks of its individual components as well as of entire workflows. Our focus is on the streaming analysis of ECEi data measured at KSTAR on NERSCs supercomputers and we routinely achieve data transfer rates of about 500 Megabyte per second. We show that a demanding turbulence analysis workload

02:08 The Miracle Cure for All Our Energy Woes?

In “The Star Builders,” Arthur Turrell explores the attempt to produce clean and abundant energy through nuclear fusion.

22.08.2021
20:43 Cross-pollinating physicists use novel technique to improve the design of facilities that aim to harvest fusion energy

Scientists have transferred a technique from one realm of plasma physics to another to enable the more efficient design of powerful magnets for doughnut-shaped fusion facilities known as tokamaks.

21.08.2021
19:59 This Week’s Awesome Tech Stories From Around the Web (Through August 21)

ENERGY Laser Fusion Experiment Unleashes an Energetic Burst of Optimism Kenneth Chang | The New York Times “Researchers at Lawrence Livermore National Laboratory reported on Tuesday that by using 192 gigantic lasers to annihilate a pellet of hydrogen, they were able to ignite a burst of more than 10 quadrillion watts of fusion power—energy released […]

20.08.2021
23:46 Cross-pollinating physicists use novel technique to improve the design of facilities that aim to harvest fusion energy

Physicists are like bees—they can cross-pollinate, taking ideas from one area and using them to develop breakthroughs in other areas. Scientists at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) have transferred a technique from one realm of plasma physics to another to enable the more efficient design of powerful magnets for doughnut-shaped fusion facilities known as tokamaks. Such magnets confine and control plasma, the fourth state of matter that makes up 99 percent of the visible universe and fuels fusion reactions.

15:00 SCIENTISTS ARE ON THE EDGE OF A FUSION POWER BREAKTHROUGH

Fusion energy — to many, the holy grail of sustainable energy — is on the edge of its next breakthrough. Using a powerful laser at the National Ignition Facility (NIF) in California, researchers managed to heat up a peppercorn-sized sample of two hydrogen isotopes well past the temperature of the Sun’s core, a process known as inertial confinement fusion (ICF). ICF is one of two major branches of fusion energy research, with the other being “magnetic confinement fusion,” a field that has seen its own recent breakthroughs. During the experiment carried out at the NIF earlier this month, scientists managed to harvest 70 percent of the energy — about 1.35 kilojoules worth — used by the powerful laser to start the fusion reaction inside the reactor, the BBC reports.

03:44 Michio Kaku calls nuclear fusion test at national lab ‘giant step toward the holy grail of energy research’

Theoretical physicist Michio Kaku explains why a recent nuclear fusion test at a national lab was a "giant step toward the holy grail of energy research."

19.08.2021
08:24 A reduced model for compressible viscous heat-conducting multicomponent flows. (arXiv:2108.08225v1 [math.NA])

In the present paper we propose a reduced temperature non-equilibrium model for simulating multicomponent flows with inter-phase heat transfer, diffusion processes (including the viscosity and the heat conduction) and external energy sources. We derive three equivalent formulations for the proposed model. All the three formulations assume velocity and pressure equilibrium across the material interface. These equivalent forms provide different physical perspectives and numerical conveniences. Temperature equilibration and continuity across the material interfaces are achieved with the instantaneous thermal relaxation. Temperature equilibrium is maintained during the heat conduction process. The proposed models are proved to respect the thermodynamical laws. For numerical solution, the model is split into a hyperbolic partial differential equation (PDE) system and parabolic PDE systems. The former is solved with the high-order Godunov finite volume method that ensures the

07:15 A reduced model for compressible viscous heat-conducting multicomponent flows. (arXiv:2108.08225v1 [math.NA])

In the present paper we propose a reduced temperature non-equilibrium model for simulating multicomponent flows with inter-phase heat transfer, diffusion processes (including the viscosity and the heat conduction) and external energy sources. We derive three equivalent formulations for the proposed model. All the three formulations assume velocity and pressure equilibrium across the material interface. These equivalent forms provide different physical perspectives and numerical conveniences. Temperature equilibration and continuity across the material interfaces are achieved with the instantaneous thermal relaxation. Temperature equilibrium is maintained during the heat conduction process. The proposed models are proved to respect the thermodynamical laws. For numerical solution, the model is split into a hyperbolic partial differential equation (PDE) system and parabolic PDE systems. The former is solved with the high-order Godunov finite volume method that ensures the

18.08.2021
20:18 Fusion experiment breaks record, blasts out 10 quadrillion watts of energy

Scientists used an unconventional method of creating nuclear fusion to yield a record-breaking burst of energy of more than 10 quadrillion watts, by firing intense beams of light from the world's largest lasers at a tiny pellet of hydrogen.

18:20 Huge lasers make conditions at the cusp of ignition for nuclear fusion

The immense lasers at the US National Ignition Facility have created the highest-pressure conditions ever made in a laboratory, bringing us a step closer to clean nuclear power

10:58 Nuclear scientists hail US fusion breakthrough

Nuclear scientists using lasers the size of three football fields said Tuesday they had generated a huge amount of energy from fusion, possibly offering hope for the development of a new clean energy source.

10:01 On 2D Harmonic Extensions of Vector Fields and Stellarator Coils. (arXiv:2108.07643v1 [math.AP])

We consider a problem relating to magnetic confinement devices known as stellarators. Plasma is confined by magnetic fields generated by current-carrying coils, and here we investigate how closely to the plasma they need to be positioned. Current-carrying coils are represented as singularities within the magnetic field and therefore this problem can be modelled mathematically as finding how far we can harmonically extend a vector field from the boundary of a domain. For this paper we consider two-dimensional domains with real analytic boundary, and prove that a harmonic extension exists if and only if the boundary data satisfies a combined compatibility and regularity condition. Our method of proof uses a generalisation of a result of Hadamard on the Cauchy problem for the Laplacian. We then provide a lower bound on how far we can harmonically extend the vector field from the boundary via the Cauchy--Kovalevskaya Theorem.

03:08 US lab stands on threshold of key nuclear fusion goal

A US science institute is on the verge of achieving a longstanding goal in nuclear fusion research.

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