bluuebunny/crossref_metadata_2025 huggingface dataset:
Tungsten transport due to the neoclassical toroidal viscosity induced by resonant magnetic perturbation in the EAST tokamak
Authors: Chang Youyou , Sun Youwen , Sheng Hui , Yan Xingting , Li Hanhui , Xie Pengcheng , Zhang Wenmin , Guo Jin , Mao Shifeng , Liu Yueqiang , Zhang Ling , Duan Yanmin , Li Yingying , Zang Qing , Ye Minyou
Published: December 2023

The impact of neoclassical toroidal viscosity (NTV) induced by resonant magnetic perturbations (RMPs) on tungsten has been studied in EAST. Modeling results from NTVTOK show that the NTV effect on low charge state tungsten ions is greater than that on high charge state tungsten ions, and the effective transport coefficient of W20+ is only on the order of 10−4–10−3 m2/s, while the effective transport coefficient of W1+ can reach 0.1–1  m2/s, which is close to the 2D neoclassical diffusion coefficient of W1+. The flux of W1+ exhibits a clear phase dependence on the RMP spectrum, and the W1+ flux obtained from NTVTOK is higher at low toroidal mode numbers compared to high toroidal mode numbers of RMPs, which is qualitatively consistent with EAST experiments.
Deep learning-based surrogate models for parametrized PDEs: Handling geometric variability through graph neural networks
Authors: Franco Nicola Rares , Fresca Stefania , Tombari Filippo , Manzoni Andrea
Published: December 2023

Mesh-based simulations play a key role when modeling complex physical systems that, in many disciplines across science and engineering, require the solution to parametrized time-dependent nonlinear partial differential equations (PDEs). In this context, full order models (FOMs), such as those relying on the finite element method, can reach high levels of accuracy, however often yielding intensive simulations to run. For this reason, surrogate models are developed to replace computationally expensive solvers with more efficient ones, which can strike favorable trade-offs between accuracy and efficiency. This work explores the potential usage of graph neural networks (GNNs) for the simulation of time-dependent PDEs in the presence of geometrical variability. In particular, we propose a systematic strategy to build surrogate models based on a data-driven time-stepping scheme where a GNN architecture is used to efficiently evolve the system. With respect to the majority of surrogate models, the proposed approach stands out for its ability of tackling problems with parameter-dependent spatial domains, while simultaneously generalizing to different geometries and mesh resolutions. We assess the effectiveness of the proposed approach through a series of numerical experiments, involving both two- and three-dimensional problems, showing that GNNs can provide a valid alternative to traditional surrogate models in terms of computational efficiency and generalization to new scenarios.
Andreev reflection imaging and longitudinal shift of pseudospin-1 fermions in two-dimensional materials
Authors: Wang Xing , Li Yu-Xian
Published: December 2023

In this work, the transverse magnetic focusing of pseudospin-1 fermions in two-dimensional materials with a superconducting contact is studied. The transmission coefficient and Andreev reflection coefficient from injector lead to collector lead are calculated based on the Landauer–Büttiker formula combined with the non-equilibrium Green's function method. The local state density diagrams of the electron and hole transmission in the center region are given, which provide a complete image of the ballistic electron–hole cyclotron orbits. Using these images, the positive longitudinal shift in Andreev reflection of the pseudospin-1 fermion can be obviously seen. Furthermore, it is found that the total offset is proportional to the number of Andreev reflection occurrence. This setup provides the possibility of detecting this shift experimentally.
Well-posedness and dynamics of stochastic retarded FitzHugh-Nagumo lattice systems
Authors: Zhang Qiangheng
Published: December 2023

This paper is concerned with the well-posedness as well as the time-dependent property of pullback random attractors for stochastic FitzHugh-Nagumo lattice systems with non-autonomous forcing terms, constant delay and multiplicative noise. First, we establish the well-posedness of such systems, which ensures the existence of a continuous non-autonomous random dynamical system. Next, the existence, uniqueness, forward compactness and long-time stability of pullback random attractors are proved. Eventually, we establish their upper semicontinuity as the time parameter tends to positive infinity and the delay time approaches zero, respectively.
An efficient numerical simulation of coupled thermo-hydro-mechanical processes in deep shale gas reservoirs
Authors: Li Jinlong , Yao Jun , Huang Zhaoqin , Wang Weihong , Wu Yu-Shu
Published: December 2023

Shale gas is an important unconventional natural gas resource, and it is an important target of exploration and development in recent years. Deep shale gas reservoirs are high-temperature, high-pressure, and high-stress with complex gas–water relationships, and its occurrence and flow mechanisms are still unclear, making the simulation of deep shale gas reservoirs still a challenging problem. Aiming at the key problems faced in the development of deep shale gas reservoirs, this paper establishes a flow-stress-temperature field coupled mathematical model and numerical model, studies the thermo-hydro-mechanical sequential decoupling method to solve the coupled mathematical model, and forms a multi-field coupled simulation technology which provides theoretical support for the development of deep shale gas. Based on the thermo-hydro-mechanical coupling calculation module, this paper conducts research on the influence of thermo-hydro-mechanical multi-field coupling parameters on the production and development performance, and it further verifies the practicability of our proposed model in real reservoirs.
Demonstration of a two-dimensional divertor Thomson scattering system on DIII-D
Authors: Schroeder Paul J. , Glass Fenton , O’Gorman Thomas , Carlstrom Thomas N. , McLean Adam G. , Watkins Matthias , Taussig Doug A. , Boivin Rejean L.
Published: December 2023

Recent upgrades providing two-dimensional divertor Thomson scattering (DTS-2D) measurements of Te and ne during a DIII-D plasma shot and a thorough description of system components and their functionality are presented. This system expands the capabilities of the existing single divertor Floor measurement location by introducing seven additional laser beam path options in the poloidal plane, spanning major radii from 1.062 to 1.335 m. The system redirects ∼1 J, 50 Hz Nd:YAG laser pulses to the new beam paths within 20 ms, stepping through each path on the divertor Floor every 200 ms during a plasma shot. The laser is redirected using an ex-vessel, fast-steering mirror to one of eight in-vessel beam paths oriented underneath the vessel tiles. Up to eleven measurement positions per beam path, from −1.35 to −1.13 m below the machine midplane, are available by dynamically refocusing the ex-vessel collection fiber array using a high-speed linear stage. Current measurement positions above the divertor Shelf are retained via a hole in the fixed, in-vessel mirror, allowing laser pass through.
Joint quantum sensing of vector magnetic field and temperature with nitrogen-vacancy centers in diamond
Authors: Zhu Xinyi , Wu Hao , Chen Bao , Yu Tongpo , Qian Peng , Fan Jing-Wei , Chen Bing
Published: December 2023

Quantum diamond sensors of multiple physical quantities, especially magnetic field and temperature, have great application prospects in materials science, biophysics, and engineering. Here, we demonstrate joint quantum sensing of vector magnetic field and temperature based on ensembles of nitrogen-vacancy (NV) centers in the diamond. Combining the intrinsic C3ν symmetry of diamond crystals and multiple-frequency excitation technique, we can directly measure frequency-modulated optically detected magnetic resonance (ODMR) spectra for three different NV orientations and then obtain the Zeeman splittings and the center frequency movement of the electron spin. An artificially generated underlying external magnetic field is used to eliminate degeneracy and facilitate coordinate transformations. The six-channel phase-locked amplification technology enables simultaneous detection of vector magnetic field and temperature with a sensitivity of about 0.87 nT/Hz1/2 and 0.32 mK/Hz1/2; meanwhile, a 23 dB isolation between magnetic and temperature signals is kept. This technology realizes the simultaneous measurement of arbitrary vector magnetic field and temperature, which can be used to study the temperature-varying magnetic phase transition.
Molecular structural dynamics in water–ethanol mixtures: Spectroscopy with polarized neutrons simultaneously accessing collective and self-diffusion
Authors: Morbidini Riccardo , Edkins Robert M. , Devonport Mark , Nilsen Gøran , Seydel Tilo , Edkins Katharina
Published: December 2023

Binary mixtures of water with lower alcohols display non-linear phase behaviors upon mixing, which are attributed to potential cluster formation at the molecular level. Unravelling such elusive structures requires investigation of hydrogen-bonding sub-nanosecond dynamics. We employ high-resolution neutron time-of-flight spectroscopy with polarization analysis in combination with selective deuteration to study the concentration-dependent structural dynamics in the water rich part of the phase diagram of water–ethanol mixtures. This method enables simultaneous access to atomic correlations in space and time and allows us to separate spatially incoherent scattering probing self-diffusion of the ethanol fraction from the coherent scattering probing collective diffusion of the water network as a whole. Our observations indicate an enhanced rigidity of the hydrogen bond network at the mesoscopic length scale compared to the molecular scale as the ethanol fraction increases, which is consistent with the hypothesis of clusters.
Numerical simulation of hypersonic flat-plate boundary-layer blowing control
Authors: Li Zongxian , Liu Meikuan , Han Guilai , Wang Dagao , Jiang Zonglin
Published: December 2023

Air-blowing is one of the techniques for active flow control and thermal protection system of hypersonic vehicles. Introducing air into the hypersonic boundary layer alters the cross-sectional profile of the boundary layer, thereby influencing the boundary-layer transition. This study investigates the active air-blowing control effects on the hypersonic flat-plate boundary layer under various blowing mass flow rates and incoming Mach numbers by solving the Reynolds-averaged Navier–Stokes equations with the Langtry–Menter four-equation transitional shear stress transport model. The study examined alterations in the blowing boundary-layer profiles under two conditions: natural and bypass transition, induced by different blowing flow rates. Blowing significantly alters the sonic line and boundary-layer profile characteristics, triggering blowing oblique shock and causing alterations in the instability mechanisms of the two transition states. A higher Mach number intensifies compressibility effects, stabilizing the boundary layer and leading to an increase in the thickness of the blowing boundary layer and air film.
Particle deposition dynamics in evaporating droplets using lattice Boltzmann and magnetic particle simulation
Authors: Lee Hee Min , Lee Joon Sang
Published: December 2023

Herein, a simulation model is proposed that combines the lattice Boltzmann method (LBM) and a magnetic particle model to observe particle ring patterns in evaporating sessile droplets, controlling them using a magnetic field. Brownian dynamics and van der Waals force models are applied to the nanoparticles. The interactions between the magnetic particles are simulated using the magnetic particle model, which is validated using previous experimental particle distribution results. The particle deposition patterns are compared according to the substrate wetting conditions. The distribution exhibited a clear coffee-ring pattern as the pinning time of the contact line increased. In the case of a non-pinned droplet, the thermal Marangoni flow was maintained, and the adhesion of the particles was delayed by the vortex. A thick, uniform ring pattern was formed when a magnetic field was applied to the particles. The particle bundles formed by the magnetic field were resistant to flow. To verify this result, the average particle velocity was measured. Consequently, particle transfer was classified into three stages. In Stage I, capillary force dominates, Marangoni flow develops in Stage II, and particle adhesion occurs in Stage III. With an increase in the magnetic strength, the velocity change exhibited a decrease across all stages.
Strong electron correlation from partition density functional theory
Authors: Shi Yi , Shi Yuming , Wasserman Adam
Published: December 2023

Standard approximations for the exchange–correlation functional in Kohn–Sham density functional theory (KS-DFT) typically lead to unacceptably large errors when applied to strongly correlated electronic systems. Partition-DFT (PDFT) is a formally exact reformulation of KS-DFT in which the ground-state density and energy of a system are obtained through self-consistent calculations on isolated fragments, with a partition energy representing inter-fragment interactions. Here, we show how typical errors of the local density approximation (LDA) in KS-DFT can be largely suppressed through a simple approximation, the multi-fragment overlap approximation (MFOA), for the partition energy in PDFT. Our method is illustrated on simple models of one-dimensional strongly correlated linear hydrogen chains. The MFOA, when used in combination with the LDA for the fragments, improves LDA dissociation curves of hydrogen chains and produces results that are comparable to those of spin-unrestricted LDA, but without breaking the spin symmetry. MFOA also induces a correction to the LDA electron density that partially captures the correct density dimerization in strongly correlated hydrogen chains. Moreover, with an additional correction to the partition energy that is specific to the one-dimensional LDA, the approximation is shown to produce dissociation energies in quantitative agreement with calculations based on the density matrix renormalization group method.
Implementation of a drive cylinder for low collisional experiments on magnetic reconnection
Authors: Gradney P. , Egedal J. , Barnhill I. , Flores-García R. , Greess S. , Kuchta C. , Olson J. , Wallace J. , Yu X. , Forest C.
Published: December 2023

For most laboratory plasma experiments, Coulomb collisions between the particle species are sufficiently frequent that the particle distribution functions are relaxed to a near-Maxwellian form. This hampers the applicability of such experiments to phenomena observed in tenuous and near-collisionless space plasma. The Terrestrial Reconnection EXperiment (TREX) at the Wisconsin Plasma Physics Laboratory aims to study collisionless reconnection for parameters relevant to the Earth’s magnetosphere. To reduce the role of collisional effects, a reconnection Drive Cylinder has been developed, which increases both the effective system size of the TREX configuration and the rate at which reconnection can be driven. These two effects now permit TREX to reach a kinetic reconnection regime where collisional effects are minimized. The Drive Cylinder is comprised of 12 single loop drive-coils connected in parallel to a 10 kV capacitor bank. Insulated sheets of aluminum are applied to smooth the magnetic fields and enhance the drive efficiency. Following is a description of the technical details and performance of the Drive Cylinder.
Toward discretization-consistent closure schemes for large eddy simulation using reinforcement learning
Authors: Beck Andrea , Kurz Marius
Published: December 2023

This study proposes a novel method for developing discretization-consistent closure schemes for implicitly filtered large eddy simulation (LES). Here, the induced filter kernel and, thus, the closure terms are determined by the properties of the grid and the discretization operator, leading to additional computational subgrid terms that are generally unknown in a priori analysis. In this work, the task of adapting the coefficients of LES closure models is thus framed as a Markov decision process and solved in an a posteriori manner with reinforcement learning (RL). This optimization framework is applied to both explicit and implicit closure models. The explicit model is based on an element-local eddy viscosity model. The optimized model is found to adapt its induced viscosity within discontinuous Galerkin (DG) methods to homogenize the dissipation within an element by adding more viscosity near its center. For the implicit modeling, RL is applied to identify an optimal blending strategy for a hybrid DG and finite volume (FV) scheme. The resulting optimized discretization yields more accurate results in LES than either the pure DG or FV method and renders itself as a viable modeling ansatz that could initiate a novel class of high-order schemes for compressible turbulence by combining turbulence modeling with shock capturing in a single framework. All newly derived models achieve accurate results that either match or outperform traditional models for different discretizations and resolutions. Overall, the results demonstrate that the proposed RL optimization can provide discretization-consistent closures that could reduce the uncertainty in implicitly filtered LES.
Operation characteristics of a ten-cavity magnetron with TE11 mode extracted through compact diffraction output
Authors: Liu Haixia , Meng Lin , Zhang Husen , Bi Liangjie , Song Minsheng , Qin Yu , Wang Bin , Li Hailong , Yin Yong
Published: December 2023

We present the design of a ten-cavity magnetron extracting TE11 mode through compact diffraction output (DO). The effect of the DO length and the depth of the vane on the performance of the ten-cavity magnetron are investigated to design a compact diffraction structure. By adjusting the length of DO and the depth of the tapered cavities to provide a proper Qe, the output power and power conversion efficiency have been improved. Simulation results of the magnetron with compact DO (the length of the DO is about half an operation wavelength) show that the microwave power of 3.0 MW with a high conversion efficiency of 62% at 2.4 GHz is achieved under an applied voltage of 56 kV and a fixed axial magnetic field of 0.24 T. In addition, the length of the anode block is designed longer than the cathode to arrange the permanent magnet, which has been preliminary designed.
Higher-order anharmonicity and strain impact on the lattice thermal conductivity of monolayer InTe
Authors: Peng Hua , Jin Liyan , Li Xiaoxue , Yang Huiying , Chen Gang
Published: December 2023

In this work, we calculated the lattice thermal conductivity of monolayer InTe by means of phonon Boltzmann transport theory with first-principles calculated inter-atomic force constants. The higher-order phonon anharmonicity was found to play a strong impact on thermal transport in InTe. With the involvement of the phonon–phonon scattering process up to the fourth-order, the in-plane lattice thermal conductivity of monolayer InTe is 5.1 W m−1 K−1 at room temperature, which is 35% of that considering only third-order force constants. Furthermore, strain was found to be an effective way to manipulate the thermal transport in InTe, which reduces to one half when applying 5% in-plane tensile strain. The strain adjustment is due to the decreases in the phonon group velocity as well as the increase in the phonon scattering rates. These findings can enrich thermal transport properties of group-III monochalcogenides and benefit the material design of thermoelectrics and thermal management electronic devices.
Smoothed particle hydrodynamics simulations of the evaporation of suspended liquid droplets
Authors: Díaz-Damacillo Lamberto , Sigalotti Leonardo Di G. , Alvarado-Rodríguez Carlos E. , Klapp Jaime
Published: December 2023

The ordinary evaporation and explosive vaporization of equilibrium, van der Waals, liquid drops subjected to uniform heating at supercritical temperatures are investigated by means of numerical simulations with the aid of a modified version of the DualSPHysics code. The models include the effects of surface tension, thermocapillary forces, mass transfer across the interface, and liquid–vapor interface dynamics by means of a diffuse-interface description. In contrast to previous simulations in this line, a new non-classical source term has been added to the internal energy equation to deal with the vaporization rate through the diffuse interface. This term is related to the diffusion of the latent heat in the interface zone and is, therefore, necessary for a correct physical description of the liquid–vapor interface structure. As the heating temperature increases the drops undergo surface evaporation, nucleation of an interior vapor bubble, nucleation followed by fragmentation of the liquid, and explosive vaporization. Heating at supercritical temperatures brings the drop out of equilibrium and forces it to rapid quenching into either the miscibility gap, where it undergoes surface evaporation by spinodal decomposition, or the metastable region bounded by the binodal and spinodal curves, where it nucleates a vapor bubble. The results also indicate that at comparable heating, drops of lower density experience faster evaporation rates than drops of higher density.
Smoothed particle hydrodynamics simulations of microstructure induced stress overshoot in structured fluids
Authors: Le-Cao K. , Tran-Duc T. , Mai-Duy N. , Quoc Nguyen Y , Khoo B. C. , Phan-Thien N.
Published: December 2023

Stress overshoot/undershoot is an important phenomenon in structured fluids undergoing dynamic and transient flow. To accurately capture the flow process, it is important to have a better understanding of and include in the numerical modeling the microstructure evolution that leads to the stress overshoot/undershoot phenomenon. We present a procedure for incorporating a microstructure model into a Lagrangian framework based on the smoothed particle hydrodynamics fluid solver. The numerical simulation is performed for a typical structured fluid under an applied strain rate history flow. Good agreement between the numerical results and the experimental data lends credence to and validates the proposed procedure for simulations of complex mixture flows. Additionally, the interaction between a flow of structured fluid and a circular cylinder placed in a channel is investigated. The viscous force is found to overshoot together with the applied gradient pressure and decrease over time as the fluid approaches the equilibrium state.
Tax thresholds yield multiple optimal cooperation levels in the spatial public goods game
Authors: Li Xiaogang , Wang Wei , Ma Yongjuan , An Xingyu , Wang Ting , Shi Lei
Published: December 2023

Income redistribution, which involves transferring income from certain individuals to others, plays a crucial role in human societies. Previous research has indicated that tax-based redistribution can promote cooperation by enhancing incentives for cooperators. In such a tax system, all individuals, irrespective of their income levels, contribute to the tax system, and the tax revenue is subsequently redistributed to everyone. In this study, we relax this assumption by introducing a tax threshold, signifying that only individuals with incomes exceeding the threshold will be subject to taxation. In particular, we employ the spatial public goods game to investigate the influence of tax rates—the percentage of income allocated to tax—and tax thresholds, which determine the income level at which individuals become taxable, on the evolution of cooperation. Our extensive numerical simulations disclose that tax thresholds produce complex outcomes for the evolution of cooperation, depending on tax rates. Notably, at low tax rates (i.e., below 0.41), as the tax threshold increases, discontinuous phase transitions in cooperation performance suggest the presence of multiple intervals of effective tax thresholds that promote peak cooperation levels. Nevertheless, irrespective of the chosen tax rate, once the tax threshold surpasses a critical threshold, the redistribution mechanism fails, causing the collapse of cooperation. Evolutionary snapshots show that self-organized redistribution forms an intermediary layer on the peripheries of cooperative clusters, effectively shielding cooperators from potential defectors. Quantitative analyses shed light on how self-organized redistribution narrows the income gap between cooperators and defectors through precise identification of tax-exempt entities, thereby amplifying the cooperative advantage. Collectively, these findings enhance our comprehension of how income redistribution influences cooperation, highlighting the pivotal role of tax thresholds.
Correlation between fragility and surface glass transition temperature of polymers
Authors: Ma Zongyi , Nie Haoran , Yan Jinsong , Tsui Ophelia K. C.
Published: December 2023

The fragility of glass describes how rapidly its molecules slow down as it is cooled near its glass transition temperature. In nanoscale films, polymer glasses with higher fragility experience larger reductions in their Tg compared to those with lower fragility. We investigated whether this is due to the free surface of the polymers, which can cause the surface Tg (Tgsurf) to decrease relative to the bulk Tg. By measuring Tgsurf of various polymers, we found that the shift in Tgsurf relative to the bulk Tg increased with fragility. This suggests that more fragile polymers are more susceptible to the free surface effect. We explain this using the concept of energy landscape, as it is used to explain the different slowdown rates between strong and fragile glass formers at Tg.
Everyday language input and production in 1,001 children from six continents
Authors: Bergelson Elika , Soderstrom Melanie , Schwarz Iris-Corinna , Rowland Caroline F. , Ramírez-Esparza Nairán , R. Hamrick Lisa , Marklund Ellen , Kalashnikova Marina , Guez Ava , Casillas Marisa , Benetti Lucia , Alphen Petra van , Cristia Alejandrina
Published: December 2023

Language is a universal human ability, acquired readily by young children, who otherwise struggle with many basics of survival. And yet, language ability is variable across individuals. Naturalistic and experimental observations suggest that children’s linguistic skills vary with factors like socioeconomic status and children’s gender. But which factors really influence children’s day-to-day language use? Here, we leverage speech technology in a big-data approach to report on a unique cross-cultural and diverse data set: >2,500 d-long, child-centered audio-recordings of 1,001 2- to 48-mo-olds from 12 countries spanning six continents across urban, farmer-forager, and subsistence-farming contexts. As expected, age and language-relevant clinical risks and diagnoses predicted how much speech (and speech-like vocalization) children produced. Critically, so too did adult talk in children’s environments: Children who heard more talk from adults produced more speech. In contrast to previous conclusions based on more limited sampling methods and a different set of language proxies, socioeconomic status (operationalized as maternal education) was not significantly associated with children’s productions over the first 4 y of life, and neither were gender or multilingualism. These findings from large-scale naturalistic data advance our understanding of which factors are robust predictors of variability in the speech behaviors of young learners in a wide range of everyday contexts.
Activation of caspase-9 on the apoptosome as studied by methyl-TROSY NMR
Authors: Sever Alexander I. M. , Alderson T. Reid , Rennella Enrico , Aramini James M. , Liu Zi Hao , Harkness Robert W. , Kay Lewis E.
Published: December 2023

Mitochondrial apoptotic signaling cascades lead to the formation of the apoptosome, a 1.1-MDa heptameric protein scaffold that recruits and activates the caspase-9 protease. Once activated, caspase-9 cleaves and activates downstream effector caspases, triggering the onset of cell death through caspase-mediated proteolysis of cellular proteins. Failure to activate caspase-9 enables the evasion of programmed cell death, which occurs in various forms of cancer. Despite the critical apoptotic function of caspase-9, the structural mechanism by which it is activated on the apoptosome has remained elusive. Here, we used a combination of methyl-transverse relaxation-optimized NMR spectroscopy, protein engineering, and biochemical assays to study the activation of caspase-9 bound to the apoptosome. In the absence of peptide substrate, we observed that both caspase-9 and its isolated protease domain (PD) only very weakly dimerize with dissociation constants in the millimolar range. Methyl-NMR spectra of isotope-labeled caspase-9, within the 1.3-MDa native apoptosome complex or an engineered 480-kDa apoptosome mimic, reveal that the caspase-9 PD remains monomeric after recruitment to the scaffold. Binding to the apoptosome, therefore, organizes caspase-9 PDs so that they can rapidly and extensively dimerize only when substrate is present, providing an important layer in the regulation of caspase-9 activation. Our work highlights the unique role of NMR spectroscopy to structurally characterize protein domains that are flexibly tethered to large scaffolds, even in cases where the molecular targets are in excess of 1 MDa, as in the present example.
Brand transcreation as multimodal configuration
Authors: Wang Junchao , Li Min
Published: December 2023

This article aims at revisiting some key concepts in translation studies by taking into account a new translation or multimodal practice called brand transcreation. Unlike translation proper or brand design proper, brand transcreation involves interlingual and multimodal production in the target culture. It relies on verbal instructions rather than the source text, and intersemiotically, transforms verbal messages into polished multimodal products. Using the systemic-functional sociosemiotic framework, this study delves into the types and dynamic orchestration of semiotic modes involved in two successful brand transcreations from a producer-researcher perspective. The findings reveal that brand transcreation can be defined as a goal-oriented textual production that configures multiple modes and serves specific communicative purposes. In this regard, the concepts of texts, contexts, meaning, and particularly (the categorization of) translation have evolved and require reconsideration.
Conceptualizing health
Authors: Scott Penelope
Published: December 2023

The word ‘health’ is highly polysemous, and many attempts have been made to define its meaning in terms of actual
use and to create a workable and even universal concept of health (Balog
1978; Boruchovitch & Mednick 2002). However, though the meaning of ‘health’
has been debated extensively, as well as the metaphorical conceptualizations of illness (e.g., Sontag 1978), there has been little treatment of how health is metaphorically conceptualized. This article
investigates the meaning of the word ‘health’ in the United States and the United Kingdom, through a search on websites based on
an examination of concordances in the Corpus of Global Web-Based English (GloWbE). It focuses on the senses
emerging from metaphorical cultural conceptualizations. Recent developments in Conceptual Metaphor Theory (Kövecses 2005; Yu 2009) and Cultural Linguistics (Palmer 1996; Sharifian 2011) have increased
the focus on the interaction between cognition and culture. I present an analysis of the conceptual metaphors, proposition
schemas, and image schemas that converge to form a cultural model for health within these speech communities revealing,
for example, that one model sees health in terms of a manageable valuable commodity, which may contribute to health
behaviors such as self-tracking and observation, as discussed by Lupton (2016).
Solving inverse scattering problems via reduced-order model embedding procedures
Authors: Zimmerling Jörn , Druskin Vladimir , Guddati Murthy , Cherkaev Elena , Remis Rob
Published: December 2023

We present a reduced-order model (ROM) methodology for inverse scattering problems in which the ROMs are data-driven, i.e. they are constructed directly from data gathered by sensors. Moreover, the entries of the ROM contain localised information about the coefficients of the wave equation. We solve the inverse problem by embedding the ROM in physical space. Such an approach is also followed in the theory of ‘optimal grids,’ where the ROMs are interpreted as two-point finite-difference discretisations of an underlying set of equations of a first-order continuous system on this special grid. Here, we extend this line of work to wave equations and introduce a new embedding technique, which we call Krein embedding, since it is inspired by Krein’s seminal work on vibrations of a string. In this embedding approach, an adaptive grid and a set of medium parameters can be directly extracted from a ROM and we show that several limitations of optimal grid embeddings can be avoided. Furthermore, we show how Krein embedding is connected to classical optimal grid embedding and that convergence results for optimal grids can be extended to this novel embedding approach. Finally, we also briefly discuss Krein embedding for open domains, that is, semi-infinite domains that extend to infinity in one direction.
Inverse design of dual-band photonic topological insulator beam splitters for efficient light transmission
Authors: Cai Chengxin , Li Xinxin , He Guangchen , Lian Feiyu , Li Mingxing , Wang Qifu , Qin Yao
Published: December 2023

The study of optical topological insulators (PTIs) has revealed intriguing optical properties that diversify the ways in which light can be manipulated, offering significant potential for a wide range of applications. This paper presents a machine learning (ML)-based approach for the reverse design of optical PTIs. Using finite element methods, the paper addresses the challenge of computing the band structure of a dual-band model, enabling the construction of a dataset suitable for ML training. With the goal of maximizing dual-band bandgaps, the study employs the random forest algorithm to predict target parameters and further designs topological edge states. Leveraging these boundary state patterns, two different optical PTI beam splitters are devised, and their transmission coefficients and losses are computed. The results demonstrate that optical devices designed using topological boundary states exhibit enhanced stability and robustness. This approach offers a reliable solution for applications in fields such as optical communication and optical sensing.
Are symmetry protected topological phases immune to dephasing? – a topological electronics perspective
Authors: Midha Siddhant , Jana Koustav , Muralidharan Bhaskaran
Published: December 2023

Harnessing topological phases with their dissipationless edge-channels coupled with the effective engineering of quantum phase transitions is a spinal aspect of topological electronics. The accompanying symmetry protection leads to different kinds of topological edge-channels which include, for instance, the quantum spin Hall (QSH) phase, and the spin quantum anomalous Hall (SQAH) phase. To model realistic devices, it is important to ratify the robustness of the dissipationless edge-channels, which should typically exhibit a perfect quantum of conductance, against various disorder and dephasing. This work is hence devoted to a computational exploration of topological robustness against various forms of dephasing. For this, we employ phenomenological dephasing models under the Keldysh non-equilibrium Green’s function formalism using a model topological device setup on a 2D-Xene platform. Concurrently, we also explicitly add disorder via impurity potentials in the channel and averaging over hundreds of configurations. To describe the extent of robustness, we quantify the decay of the conductance quantum with increasing disorder under different conditions. Our analysis shows that these topological phases are robust to experimentally relevant regimes of momentum dephasing and random disorder potentials. We note that Rashba mixing worsens the performance of the QSH phase and point out a mechanism for the same. Further, we observe that the QSH phase break downs due to spin dephasing, but the SQAH phase remains robust. The SQAH phase shows stark robustness under all the dephasing regimes, and shows promise for realistic device structures for topological electronics applications.
Robust depth-verified RGB-D visual odometry with structural regularities for indoor environments
Authors: Xing Jing , Zhong Qixue , Liu Jian
Published: December 2023

This paper proposes an RGB-D visual odometry method that leverages point, line, plane features and Manhattan structures to achieve robust frame tracking and precise pose estimation, especially in textureless scenes. A validation method is introduced that ensures accurate frame-to-frame rotation estimation by comparing rotation angles computed from multiple Manhattan structures. Depth verification methods involving parameter fitting and outlier removal for point, line, and plane features are implemented by investigating the covariance of sensor depth measurements. We also employ local bundle adjustment in the local mapping thread to refine keyframe poses and landmarks. Comprehensive ablation studies confirm the effectiveness of our contributions. Experimental results on public datasets demonstrate that our method achieves obvious advantages in accuracy and robustness while maintaining real-time performance.
Research on optimization technology of excitation coil in downhole annular flow electromagnetic measurement system
Authors: Ge Liang , Zhou Shuai , Xiao Xiaoting , Zeng Wen , Huang KaiQiang
Published: December 2023

External interference and non-uniform velocity distribution influence the downhole annular flow electromagnetic measurement, reducing detection accuracy. Optimizing the structure of the excitation coil can effectively enhance the uniformity of magnetic field distribution, constituting an essential approach for improving measurement accuracy. Aiming at the excitation coil optimization technology of annular flow electromagnetic measurement system, the study first analyzes and obtains the magnetic field model of the excitation coil based on the principle of annular flow electromagnetic measurement, combined with the theory of vector weight function and puts forward the optimization theory of annular excitation coil. Secondly, the simulation models of rectangular and saddle exciting coils are established using finite element software to analyze the magnetic field distribution of annular flow under different coil and core sizes. The optimal structural parameters of the exciting coils in the annular flow electromagnetic measurement system are determined through evaluation indexes for the magnetic field. Finally, an experimental platform was established for optimizing the electromagnetic measurement of annular flow, and this platform was used to conduct comparative experiments. The experimental results show that the maximum reduction of the optimized system is 3.192%, 1.289%, and 1.813% for the single verification relative value error, single-point verification relative value error, and repeatability compared with the rectangular excitation coil annular flow electromagnetic measurement system. In the flow measurement interval of 25–100 m3 h−1, the single-point verification of the optimized system has a maximum relative display value error of 0.378%, and the measurement accuracy reaches 1.0 level and meets the consistency of the transmission of quantity and value. The research findings hold significant guidance for enhancing the measurement accuracy of the downhole annular flow electromagnetic measurement system.
Application of deep learning in fMRI-based human brain parcellation: a review
Authors: Li Yu , Chen Xun , Ling Qinrui , He Zhiyang , Liu Aiping
Published: December 2023

Functional magnetic resonance imaging (fMRI)-based human brain parcellation reveals brain fundamental organizational principles noninvasively, providing prior guidance for functional analysis and physiological measurement of the brain. Recently, the profound success of deep learning in natural and medical images has attracted an increasing number of deep learning-based investigations for brain parcellation which have accomplished encouraging achievements. This review aims to provide researchers with a comprehensive overview of deep learning-based fMRI brain parcellation and promote the development of this emerging frontier. To begin, we present the history of brain parcellation, emphasizing the current advantages of deep learning in this field. Following a summary of the input modalities, traditional methods, and evaluation criteria for fMRI-based brain parcellation, we comprehensively review the current deep-learning approaches and categorize them based on network architectures. The datasets, models, performance, advantages and limitations are described in detail. Finally, we discuss the current challenges and future directions in methodology, neuroscience, and application, to inspire future investigation in deep learning-based brain parcellation.
A feature extraction method for rotating machinery fault diagnosis based on a target detection index and successive variational mode decomposition
Authors: Cao Chaofan , Zhang Guangtao , Li Zhongliang , Lu Na , Jiang Shuangyun , Wang Lei
Published: December 2023

Rotating machinery feature extraction is critical for the subsequent fault diagnosis and ensuring safe and stable operation. However, the commonly used methods often have limitations, such as the extracted features being redundant or insufficient and the method parameters generally being set based on experience. Although some feature extraction methods apply optimization algorithms for parameter setting, their objective functions are often too simple to obtain favorable fault diagnosis results. To address these issues, a target detection index (TDI) is constructed which can consider the discrimination among features more comprehensively and make the extracted features more sensitive. Furthermore, a fault feature extraction method is proposed based on TDI and successive variational mode decomposition (SVMD). Taking TDI as the objective function, genetic algorithm (GA) is used for the feature selection process and SVMD parameter optimization. The obtained features are then fused and visualized using t-distributed stochastic neighbor embedding and are classified using support vector machines. The Case Western Reserve University data and hydropower generating unit data are employed for method verification. When compared with other decomposition algorithms, the proposed method exhibits great ability at extracting highly sensitive features.
Green to deep-red emissive carbon dot formation by C+ ion implantation on nitrogen beam created self-masked nano-template
Authors: Bhowmick Sudip , Mukherjee Joy , Ghosal Manorama , Nayak Chumki , Satpati Biswarup , Pramanik Goutam , Karmakar Prasanta
Published: December 2023

We report the formation of green to red emissive arrays of carbon dot on silicon-nitride nano-templates by successive implantation of nitrogen and carbon broad ion beams. The patterned nano-templates are formed by 14 keV N2
+ ion-bombardment at grazing incident (70°) on Si. Subsequently, 5 keV C+ ions are implanted at the selective sites of the pyramidal nano-template by taking advantage of the self-masking effect. The nano-pyramidal pattern and the implanted carbon dots at the specific sites are confirmed by atomic force microscopy and cross sectional transmission electron microscopy measurements. The developed carbon dots (CDs) are mostly amorphous and consists of SiC and graphitic nitrogen (CN). G-band and D-band carbons are identified by Raman spectroscopy, while the presence of SiC and CN are detected by XPS measurements. A change of band-gap is observed for C-implanted templates by the UV–vis spectroscopy. Excitation wavelength-dependent photoemission from the dots is found in the green to red region. Maximum intense PL is observed in the green–orange region for excitation wavelength of 425 nm and a redshift of PL with decreasing intensity is observed with the increase of excitation wavelength. The observed photoluminescence is described in terms of the combined effects of quantum confinement, graphitic nitrogen and defect induced additional states formation in the carbon dots. The potential applications of CDs are also addressed.
Bioaerosol size as a potential determinant of airborne E. coli viability under ultraviolet germicidal irradiation and ozone disinfection
Authors: Hao Weixing , Huang Yue-Wern , Wang Yang
Published: December 2023

Ultraviolet germicidal irradiation (UVGI) and ozone disinfection are crucial methods for mitigating the airborne transmission of pathogenic microorganisms in high-risk settings, particularly with the emergence of respiratory viral pathogens such as SARS-CoV-2 and avian influenza viruses. This study quantitatively investigates the influence of UVGI and ozone on the viability of E. coli in bioaerosols, with a particular focus on how E. coli viability depends on the size of the bioaerosols, a critical factor that determines deposition patterns within the human respiratory system and the evolution of bioaerosols in indoor environments. This study used a controlled small-scale laboratory chamber where E. coli suspensions were aerosolized and subjected to varying levels of UVGI and ozone levels throughout the exposure time (2–6 s). The normalized viability of E. coli was found to be significantly reduced by UVGI (60–240 μW s cm−2) as the exposure time increased from 2 to 6 s, and the most substantial reduction of E. coli normalized viability was observed when UVGI and ozone (65–131 ppb) were used in combination. We also found that UVGI reduced the normalized viability of E. coli in bioaerosols more significantly with smaller sizes (0.25–0.5 μm) than with larger sizes (0.5–2.5 μm). However, when combining UVGI and ozone, the normalized viability was higher for smaller particle sizes than for the larger ones. The findings provide insights into the development of effective UVGI disinfection engineering methods to control the spread of pathogenic microorganisms in high-risk environments. By understanding the influence of the viability of microorganisms in various bioaerosol sizes, we can optimize UVGI and ozone techniques to reduce the potential risk of airborne transmission of pathogens.