Arbitrary Factor Research Articles

An analytical investigation of the Van Der Waals gas system: Dynamics insights into bifurcation, optical pattern along with sensitivity and chaotic analysis

This paper focuses on obtaining exact solutions for the nonlinear Van der Waals gas system using the modified Khater method. Renowned as one of the latest and most precise analytical schemes for nonlinear evolution equations, this method has proven its efficacy by generating diverse solutions for the model under consideration. The governing equation is transformed into an ordinary differential equation through a well-suited wave transformation. This analytical simplification makes it possible to use the provided methods to derive trigonometric, rational, and hyperbolic solutions. To illuminate the physical behavior of the model, graphical plots of selected solutions are presented. By selecting appropriate values for arbitrary factors, this visual representation enhances comprehension of the dynamical system. Furthermore, the system undergoes a certain transformation to become a planar dynamical system, and the bifurcation analysis is examined. Additionally, the sensitivity analysis of the dynamical system is conducted using the Runge–Kutta method to confirm that slight alterations in the initial conditions have minimal impact on the stability of the solution. The presence of chaotic dynamics in the Van der Waals gas system is explored by introducing a perturbed term in the dynamical system. Two and three-dimensional phase profiles are used to illustrate these chaotic behaviors.

From Coasting to Energy-conserving: New Self-similar Solutions to the Interaction Phase of Strong Explosions

Astrophysical explosions that contain dense and ram-pressure-dominated ejecta evolve through an interaction phase, during which a forward shock (FS), contact discontinuity (CD), and reverse shock (RS) form and expand with time. We describe new self-similar solutions that apply to this phase and are most accurate in the limit that the ejecta density is large compared to the ambient density. These solutions predict that the FS, CD, and RS expand at different rates in time and not as single temporal power laws, are valid for explosions driven by steady winds and homologously expanding ejecta, and exist when the ambient density profile is a power law with a power-law index shallower than ∼3 (specifically when the FS does not accelerate). We find excellent agreement between the predictions of these solutions and hydrodynamical simulations, both for the temporal behavior of the discontinuities and for the variation of the fluid quantities. The self-similar solutions are applicable to a wide range of astrophysical phenomena and—although the details are described in future work—can be generalized to incorporate relativistic speeds with arbitrary Lorentz factors. We suggest that these solutions accurately interpolate between the initial “coasting” phase of the explosion and the later, energy-conserving phase (or, if the ejecta is homologous and the density profile is sufficiently steep, the self-similar phase described in R. A. Chevalier).

Supramolecular Luminescent Copper-Nanocluster-Based Dough with Excellent Electrical Conductivity Sensing Properties.

In recent years, the rapid advancement of flexible conductive materials has significantly increased the demand for dough materials that offer high flexibility and conductivity for diverse applications. Here, we developed a flexible, stretchable, and self-healing dough utilizing hydrogen-bonding interactions between glutathione-stabilized copper nanoclusters (GSH-Cu NCs) and poly(acrylic acid) (PAA). The dough materials can be kneaded, readily reshaped, and further processed to create bulk materials of arbitrary form factors. The incorporation of PAA not only preserved the vibrant blue emission of GSH-Cu NCs but also enhanced their electrical conductivity and stretchability. The dough can be stretched up to 25 times its initial length and achieves complete self-healing in a short time. Moreover, the dough can automatically repair physical damage and return to its initial conductivity levels after healing. Surprisingly, the electrical conductivity of the dough can reach as high as 2.97 S/m, which is relatively superior compared to that of conventional conductive materials. This study presents a dough that serves as a highly sensitive strain sensor, capable of effectively monitoring human movement across a broad range of strains.

3D printing of customized Li-S microbatteries

Microbatteries serve as essential energy supply components in microelectronic, wearable and implantable devices. Despite recent efforts, developing lithium metal microbatteries with high energy densities and mechanical flexibility in structural design is still challenging, because of the poor interfacial stability and poor processability of lithium foil. Herein, we developed lithium-sulfur (Li-S) microbatteries with customized configurations using 3D printing techniques, which consist of a printable Li anode hosted within a graphene aerogel framework and a printable S cathode based on a carbon nanocages framework, featuring abundant porous structures and large specific surface areas. Consequently, the 3D-printed Li anode achieves over 900 hours of cycling with low overpotentials at an ultrahigh current density of 10 mA cm−2, and the 3D-printed S cathode delivers an ultrahigh capacity of 21.9 mAh cm−2 at the high thickness of 2.3 mm. Furthermore, the 3D-printed Li-S cell delivers an ultrahigh areal energy density of 48.4 mWh cm−2 at a high power density of 3.5 mW cm−2. The application potential of the 3D-printed customized Li-S microbatteries with arbitrary form factors is demonstrated by LED lighting and powering wearable sensor integration system, paving the way for their applications in miniaturized devices.

Euclidean quantum wormholes

We study wormhole as the solution of the Wheeler–DeWitt (WdW) equation satisfying Hawking–Page wormhole boundary conditions in Friedmann–Robertson–Walker (FRW) cosmology. The quantum wormholes are formulated with arbitrary factor ordering of the Hamiltonian constraint operators with perfect fluid matter sources as well as minimally coupled scalar fields.

Investigating optical soliton pattern and dynamical analysis of Lonngren wave equation via phase portraits

This study focuses on finding effective solutions to a mathematical equation known as the Lonngren wave equation. The solutions from the Lonngren wave equation can be used to evaluate electromagnetic signals in cable lines and sound waves in stochastic systems. The main equation is transformed into an ordinary differential equation by utilizing a suitable wave transformation, allowing for the exploration of mathematical models by using the modified Khater technique to detect the exact solution of a solitary wave. We use the provided method to derive the trigonometric, rational, and hyperbolic solutions. To illustrate the model’s physical behavior, we also present graphical plots of selected solutions to illustrate the physical behavior of the model. By choosing appropriate values for arbitrary factors, the visual representation enhances the understanding of the dynamical system. Furthermore, the system is transformed into a planar dynamical system, and phase portrait analysis is conducted. Additionally, the sensitivity analysis of the dynamical system confirms that slight changes in the initial conditions will have minimal impact on the stability of the solution. The existence of chaotic dynamics in the Lonngren wave equation is explored by introducing a perturbed term in the dynamical system. Two and three-dimensional phase portraits will be used to demonstrate these chaotic behaviors.

Certifying Ground-State Properties of Many-Body Systems

A ubiquitous problem in quantum physics is to understand the ground-state properties of many-body systems. Confronted with the fact that exact diagonalization quickly becomes impossible when increasing the system size, variational approaches are typically employed as a scalable alternative: Energy is minimized over a subset of all possible states and then different physical quantities are computed over the solution state. Despite remarkable success, rigorously speaking, all that variational methods offer are upper bounds on the ground-state energy. On the other hand, so-called relaxations of the ground-state problem based on semidefinite programming represent a complementary approach, providing lower bounds to the ground-state energy. However, in their current implementation, neither variational nor relaxation methods offer provable bound on other observables in the ground state beyond the energy. In this work, we show that the combination of the two classes of approaches can be used to derive certifiable bounds on the value of any observable in the ground state, such as correlation functions of arbitrary order, structure factors, or order parameters. We illustrate the power of this approach in paradigmatic examples of 1D and 2D spin-1/2 Heisenberg models. To improve the scalability of the method, we exploit the symmetries and sparsity of the considered systems to reach sizes of hundreds of particles at much higher precision than previous works. Our analysis therefore shows how to obtain certifiable bounds on many-body ground-state properties beyond energy in a scalable way. Published by the American Physical Society 2024

Spatial Differentiation Characteristics and Influencing Factors of Surface Soil Cd in Karst Plateau Area of Guiyang City

In order to explore the spatial differentiation characteristics and variation law of soil Cd content in a high geological background area, 14 421 topsoil samples were collected from topsoil in the karst area of Guiyang City. Global Moran's I index, cold hot spot analysis, semi-variance function, and Kriging interpolation were used to reveal the spatial structure and distribution law of soil Cd content. The influence of environmental factors on soil Cd content and its main controlling factors were analyzed through analysis of variance and geographic detector. The results showed that： ① The Cd content of karst surface soil in Guiyang varied from 0.03 to 1.36 mg·kg-1, with an average of 0.440 mg·kg-1, which was 1.77 times and 5.95 times the Guizhou soil Cd background value and Chinese soil Cd background value, respectively. The over-standard rate of soil Cd was 30%, which was 4.29 times that of 7% of soil Cd in China. ② There was a significant spatial positive correlation of soil Cd content, showing an aggregation trend in the global space, whereas in the local region, the northeast and southwest were hot spots, and the north was a cold spot. The nugget coefficient of soil Cd content was 10.37%, indicating that soil Cd was mainly affected by structural factors. ③ In terms of spatial distribution, soil Cd showed different accumulation trends. In some massive soils, such as Xifeng County, Xiuwen County, Qingzhen City, Huaxi District, and Nanming District, the soil ω（Cd）was less than 0.3 mg·kg-1. The soil ω（Cd）was between 0.3 and 0.6 mg·kg-1,and soil Cd in Baiyun District, Wudang District, Guanshan Lake area, and Yunyan area as a whole lied within this range. The soil ω（Cd）between 0.6 and 0.9 mg·kg-1 was concentrated in the southwest of Qingzhen City, the south of Huaxi District, and the north of Kaiyang County, whereas soil ω（Cd） between 0.9 and 1.2 mg·kg-1 was mainly concentrated in the southwest of Qingzhen City. The extreme value of soil Cd content （ > 1.2 mg·kg-1） was mostly distributed in Kaiyang County, Xiuwen County, Qingzhen City, and Huaxi District. ④ The results of analysis of variance and geo-detector showed that different environmental factors had significant effects on the spatial differentiation of soil Cd, but their explanatory power on soil Cd content varied： stratum （0.176 5） > soil type （0.026 0） > organic matter （0.025 1） > altitude （0.010 5） > parent rock （0.007 3） > land use （0.006 4） > pH （0.001 3）, and the interaction between stratum and arbitrary environmental factors was the greatest. Therefore, stratum was the main factor affecting the spatial differentiation of soil Cd content.

Advanced subject-specific neck musculoskeletal modeling unveils sex differences in muscle moment arm and cervical spine loading

Neck pain and injuries are growing healthcare burdens with women having a higher incidence rate and poorer treatment outcomes than males. A better understanding of sex differences in neck biomechanics, foundational for more targeted, effective prevention or treatment strategies, calls for more advanced subject-specific musculoskeletal modeling. Current neck musculoskeletal models are based on generic anatomy, lack subject specificity beyond anthropometric scaling, and are unable to accurately reproduce neck strengths exhibited in vivo without arbitrary muscle force scaling factors or residual torque actuators. In this work, subject-specific neck musculoskeletal models of 23 individuals (11 male, 12 female) were constructed by integrating multi-modality imaging and biomechanical measurements. Each model simulated maximal voluntary neck static exertions in three postures: neck flexion in a neutral posture, flexion in a 40° extended posture, and extension in a 40° flexed posture. Quantitative model validation showed close agreement between model-predicted muscle activation and EMG measurement. The models unveiled that (1) males have greater moment arms in one flexor muscle group and five extensor muscle groups, (2) females exhibited higher cervical spinal compression per unit exertion force in the flexed posture, and (3) the variability of compression force was much greater in females in all three exertions but most notably in the extension with a flexed “dropped head” position. These insights illuminated a plausible pathway from sex differences in neck biomechanics to sex disparities in the risk and prevalence of neck pain.

Impact Analysis of a new, Cross-sector Service Provision of Gastroenterologic Endoscopic Services in Accordance with 115f SGB V (Hybrid-DRG): Allocation Matrix and Cost Analysis

With the introduction of §115f SGB V, the prerequisites for "sector-equal remuneration" ('Hybrid DRG') have been created. In an impact analysis, we assigned inpatient gastroenterological endoscopic (GAEN) cases in a matrix of future hybrid DRG versus outpatient surgery (AOP) or inpatient treatment. In selected DRGs (G47B, G67A, G67B, G67C, G71Z, H41D, H41E) an allocation matrix of GAEN cases was created on medical grounds. For this purpose, service groups from the DGVS service catalog ('Leistungskatalog') were assigned to the groups: 'Hybrid-DRG', 'AOP' and 'Inpatient' by a group of experts based on the DGVS position paper. Cost data from the DGVS-DRG project for the 2022 data year from 36 InEK calculation hospitals with a total of 232,476 GAEN cases were evaluated. 26 service groups from the DGVS service catalog were assigned to a "Hybrid-DRG", 24 to the "inpatient" group, and 12 to the "AOP" group. 7 performance groups were splitted "depending on the OPS code" and classified at this level. Cases with additional fees were excluded from a hybrid DRG because these cannot be agreed there.The cost analysis shows that services that are already in the AOP have a similar cost level to services that have been classified as 'Hybrid-DRG'. With the cost calculation, a cost level could be presented for the hybrid DRGs formed. Based on clearly defined structural, procedural and personnel requirements, services from suitable DRGs can be transferred to a hybrid DRG. Assigning services without the involvement of clinical experts seems extremely difficult. Case assignment based on arbitrary contextual factors increases complexity without demonstrably increasing the quality of the assignment and needs to be further developed. A cost analysis can be derived from the known inpatient costs and must serve as the basis for the 2025 Hybrid DRG catalog.

Author's Reply to Comment by Greaves et al. on “Phosphine in the Venusian Atmosphere: A Strict Upper Limit From SOFIA GREAT Observations”

AbstractIn an attempt to understand the findings presented in the Comment by Greaves et al. (2023, https://doi.org/10.1029/2023GL103539), we followed their data analysis methodology, omitting the hot and cold‐load calibrations that are an important part of the standard SOFIA GREAT instrument calibration procedure. This process requires scaling of the Venus off‐source spectra by an arbitrary factor, which in turn introduces residuals of the intrinsic receiver bandpass shape as spurious components in the resulting line/continuum spectra. Although these additional artifacts can be reduced via Fourier‐domain spectral filtering, their removal depends on an ill‐constrained interpolation of the Venus continuum across the PH3 spectral line positions, resulting in an unreliable final spectrum. We therefore conclude that the PH3 lines claimed to be detected in the Comment by Greaves et al. (2023, https://doi.org/10.1029/2023GL103539) originate from data/analysis artifacts, and confirm our original result that there is no evidence for phosphine in the SOFIA Venus data.

S[formula omitted]U-PVNet: Arbitrary-scale point cloud upsampling via Point-Voxel Network based on Siamese Self-Supervised Learning

Recently, benefiting from the rapid development of deep learning, many research on point cloud upsampling task has been successfully proposed with remarkable performance. The quality of the upsampled point clouds is also vital for the quality of the reconstructed meshes in 3D surface reconstruction task. However, most existing methods either independently train a specific upsampling network for each scale factor or heavily rely on the paired training data as the supervision information. To address these limitations which are both inefficient and impractical for storage and computation in real applications, we present a Point-Voxel Network based on Siamese Self-supervised learning for arbitrary-Scale point cloud Upsampling (S3U-PVNet), which mainly includes a Down–Up pipeline and an Up–Down pipeline, to support self-supervised point cloud upsampling with arbitrary scale factors by a single network. The core of our network is a series of stacked point-voxel feature fusion (PVFF) modules, which can effectively and efficiently extract and fuse multi-granularity features from input point clouds. Each module includes two branches, a point-based branch that represents the sparse inputs in points to adaptively learn the spatial relationship among points, and a voxel-based branch that extract features in voxels to reduce the problem of inaccurate feature extraction caused by the irregularity and sparsity of point clouds. Furthermore, we also propose an end-to-end training objective for our siamese self-supervised network encapsulating reconstruction loss and similarity loss, which considers both global shape constraint and local geometric constraint. We achieve new state-of-the-art unsupervised upsampling results on various synthetic datasets and demonstrate the generalization of the proposed method on challenging real-world data.

Investigation of arbitrary drug use and factors influencing it in patients admitted to Shahid Rahimi Hospital, Khorramabad in 2021

Background: Arbitrary use of drugs not only leads to failure to treat the disease but also leads to drug complications, inconsistency of prevalence of diseases in the country with the amount of drug consumption and the problem of indiscriminate drug consumption in the pharmaceutical system of the country. Therefore, this study was conducted with the aim of investigating self-inflicted medication use factors influencing it among patients admitted to Shahid Rahimi Hospital, Khorramabad. Materials and Methods: This study was conducted on 381 patients of Shahid Rahimi Hospital, Khorramabad in winter 2021. A simple random sample was drawn and the information of the individuals was collected using a questionnaire. Descriptive statistics were used to summarize the demographic information and a t-test was conducted using SPSS 23 software for comparisons. Results: According to the results of this study, the average age of people was 34.322±11.438 years. The percentage of arbitrary drug use was 25.3%. The percentage of self -administered medication was 56.8% for painkillers and 56.2% for multivitamins, 71.2% for cold and flu patients and 38% for chronic and acute headaches. The most important reasons for self-medication are, in this order, the higher quality of foreign medicines compared to Iranian medicines and the use of medicinal plants, and the least important reasons for self-medication are fear and shame of medical examinations, lack of trust in doctors' medicine, taking medicines during illness on the advice of acquaintances and the use of previous doctors' prescriptions were mentioned. Conclusion: Given the high prevalence of self-inflicted drug use, it is suggested that various educational programs be implemented by appropriate organizations, including the Ministry of Health, Treatment and Medical Education, medical centers, and mass media to reduce the factors identified in this study.

Nontwist field line mapping in a tokamak with ergodic magnetic limiter.

For tokamaks with uniform magnetic shear, Martin and Taylor have proposed a symplectic map which has been used to describe the magnetic field lines at the plasma edge perturbed by an ergodic magnetic limiter. We propose an analytical magnetic field line map, based on the Martin-Taylor map, for a tokamak with arbitrary safety factor profile. With the inclusion of a nonmonotonic profile, we obtain a nontwist map which presents the characteristic properties of degenerate systems, such as the twin islands scenario, shearless curve, and separatrix reconnection. We estimate the width of the islands and describe their changes of shape for large values of the limiter current. From our numerical simulations about the shearless curve, we show that its position and aspect depend on the control parameters.

An Image Arbitrary-Scale Super-Resolution Network Using Frequency-domain Information

Image super-resolution (SR) is a technique to recover lost high-frequency information in low-resolution (LR) images. Since spatial-domain information has been widely exploited, there is a new trend to involve frequency-domain information in SR tasks. Besides, image SR is typically application-oriented and various computer vision tasks call for image arbitrary magnification. Therefore, in this article, we study image features in the frequency domain to design a novel image arbitrary-scale SR network. First, we statistically analyze LR-HR image pairs of several datasets under different scale factors and find that the high-frequency spectra of different images under different scale factors suffer from different degrees of degradation, but the valid low-frequency spectra tend to be retained within a certain distribution range. Then, based on this finding, we devise an adaptive scale-aware feature division mechanism using deep reinforcement learning, which can accurately and adaptively divide the frequency spectrum into the low-frequency part to be retained and the high-frequency one to be recovered. Finally, we design a scale-aware feature recovery module to capture and fuse multi-level features for reconstructing the high-frequency spectrum at arbitrary scale factors. Extensive experiments on public datasets show the superiority of our method compared with state-of-the-art methods.

Comprehensive modeling and formulation of split DC link capacitors balancing problem in Three-Phase Three-Level bidirectional AC/DC converters operating with arbitrary power factor

Three-phase three-level AC/DC and DC/AC converters have demonstrated several advantages over their classical two-level counterparts such as better efficiency, enhanced total harmonic distortion, lower dv/dt and lesser overall chip area. T-type and neutral point clamped (NPC) topologies dominate the family of three-phase three-level converters, widely used in 10kVA – 100kVA power range. However, control of three-level converters is more complicated than of their classical two-level counterparts due to necessity of adopting an additional voltage loop, aimed to balance partial split DC link voltages. Moreover, the latter contain multiples of triple-base-frequency harmonic ripple even under balanced AC-side operation (this does not happen in two-level converters), which should be minimized by the balancing loop mentioned above. The nature of partial DC link ripple formation was analyzed in-depth for unity power factor operation, yet hardly extended to non-unity power factor cases. Moreover, inaccurate conclusions were drawn in some of the related studies. Consequently, this paper focuses on comprehensive modeling and formulation of split DC link capacitors balancing problem in three-phase three-level bidirectional AC/DC converters operating with arbitrary power factor. Analytical expression for dynamics of partial DC link voltages difference is derived and studied in depth. The relation between zero-sequence component of modulation signals and neutral point current, tending to unbalance partial DC link voltages is explicitly established for arbitrary power factor values. Practical cases of restricted and unrestricted zero-sequence component of modulation signals are presented. Analytical predictions are shown to be fully supported by simulations based on both switching-cycle-averaged and full switched converter models. Experimental results obtained employing a 10kVA T-type converter prototype operating with restricted zero-sequence component of modulation signals under different power factor values are also given to further validate the presented methodology.

Generalization of Split DC Link Voltages Behavior in Three-Phase-Level Converters Operating with Arbitrary Power Factor with Restricted Zero-Sequence Component

This article examines the impact of a power factor on the behavior of partial DC link voltages in three-phase three-level AC/DC (or DC/AC) converters operating without additional balancing hardware. We consider the case in which the controller utilizes a bandwidth-restricted (DC in steady state) zero-sequence component to achieve average partial DC link voltage equalization since the injection of high-order zero-sequence components is impossible or forbidden. An assessment of partial split DC-link capacitor voltage behavior (particularly that of ripple magnitudes and phases) is necessary for, e.g., minimizing the values of DC link capacitances and selecting reference voltage values. Previous studies assessed the abovementioned behavior analytically for operation under a unity power factor based on third-harmonic-dominated split partial voltages’ ripple nature. However, it is shown here that deviation from the unity power factor introduces additional (to the third harmonic) non-negligible harmonic content, increasing partial voltage ripple magnitudes and shifting their phase (relative to the mains voltages). As a result, the third-harmonic-only assumption is no longer valid, and it is then nearly impossible to derive corresponding analytical expressions. Consequently, a numerical approach is used in this work to derive a generalized expression of normalized ripple energy as a function of the power factor, which can then easily be utilized for assessments of split DC link voltage behaviors for certain DC link capacitances and reference voltages. Simulations and experimental results validate the proposed methodology applied to a 10 kVA T-type converter prototype.

Acute bioaccumulation and chronic toxicity of olivine in the marine amphipod Gammarus locusta

Active atmospheric carbon dioxide removal (CDR) is needed at a gigaton scale in the next decades to keep global warming below 1.5 °C. Coastal enhanced silicate weathering (CESW) aims to increase natural ocean carbon sequestration via chemical weathering of finely ground olivine (MgxFe(1−x)SiO4) rich rock dispersed in dynamic coastal environments. However, the environmental safety of the technique remains in question due to the high Ni and Cr content of olivine. Therefore, we investigated the short term bioaccumulation and chronic toxicity of olivine in the marine amphipod Gammarus locusta. Acute 24-h olivine exposure resulted in significant grain size dependent olivine ingestion and subsequent Ni and Cr accumulation in tissues. Thousands of small (mainly ≤ 10 µm) olivine grains were ingested by G. locusta, but their importance for trace metal bioaccumulation requires additional research. Most olivine grains were egested within 24 h. Chronic 35-day olivine (3–99 µm) exposure reduced amphipod survival, growth, and reproduction, likely as a result of metal induced oxidative stress and disturbance of major cation homeostasis. Amphipod reproduction was significantly reduced at olivine concentrations of 10% w/w and higher. In the context of ecological risk assessment, application of an arbitrary assessment factor of 100 to the highest no observed effect concentration of 1% w/w olivine yields a very low predicted no-effect concentration (PNEC) of 0.01% w/w olivine. This low PNEC value highlights the urgent need for additional marine olivine toxicity data to accurately assess the environmentally safe scale of coastal enhanced weathering for climate change mitigation.

MCI-HyperNet: A multiple contextual information-based adaptive weight learning network for controllable image reconstruction

Contemporary deep learning methods for image reconstruction have shown promising results over classical methods. However, they are not robust to image alterations due to deviations in the imaging conditions at test time. The training process does not account for explicit variations in the contextual information about the imaging task, such as image control parameters and input settings that could hold a causal relationship to the visual content of the reconstructed image. In this work, we propose dynamic weight prediction (DWP) networks to learn the contextual settings and the relationships between them. This model-based meta-learning approach would offer two valuable capabilities in a single network - (i) scalability to multiple input settings and (ii) tunability to continuously varying control settings with the knowledge of a few settings. The proposed network, MCI-HyperNet, is a controllable image reconstruction network with DWP sub-networks conditioned on multiple contextual settings. The proposed approach strikes a balance between reliable context-adaptive weight learning and context-agnostic weight learning to obtain robust image reconstructions. We have extensively experimented with our network for accelerated MRI reconstruction considering the essential research directions provided in the fastMRI challenge results. The proposed method exhibits (i) superior reconstruction quality with improvement margins of ∼0.5 dB in PSNR and ∼0.01 in SSIM over adaptive reconstruction methods for cardiac MRI and multiple brain MRI contrasts, (ii) better scalability to multiple anatomies, contrasts, and under-sampling mask patterns with accuracy improvement margins of ∼0.2 dB in PSNR and ∼0.005 in SSIM over joint training and context-specific models for knee MRI, and (iii) generality to 40 arbitrary acceleration factors over joint training when trained on just five acceleration factors. Our model can learn multiple contrast sequences through continual learning without catastrophically forgetting old MRI sequences. Furthermore, our method is benchmarked for reconstruction quality against other MRI reconstruction architectures trained for a specific contextual setting. Besides, an interpretability analysis using a relational knowledge distillation measure shows that our model exhibits superior context-specific and relational knowledge across contexts over jointly trained models. Our source code is publicly available at https://github.com/sriprabhar/MCI-HyperNet.

Kappa vacua: enhancing the Unruh temperature

We provide further elaboration on kappa mode, which is a mode constructed by the linear combination of Rindler modes in the right and the left Rindler wedges, exhibiting norms with opposite signs. We establish a relation among different kappa vacua, resembling the thermofield double state. However, the energy of a kappa photon no longer exhibits a linear dependence on its frequency, unless the limit of κ → 0 (the Rindler vacuum) is taken into account. In other words, a kappa vacuum can be expressed in terms of the Rindler vacuum as the conventional thermofield double state, with the usual energy for a photon. However, it features a modified Unruh temperature given by {T}_{kappa }=frac{mathit{hslash a}}{2pi c{k}_B}kappa . Consequently, when a uniformly accelerated observer with an acceleration a is immersed in a κ-vacuum, they perceive a thermal bath. However, the temperature experienced by the observer is a modified Unruh temperature denoted as Tκ. Remarkably, the Unruh temperature can be enhanced by an arbitrary factor of κ.