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Velocity gauge for oscillator strength in ΔSCF theory
Delta self-consistent-field (ΔSCF) theory is widely used for electronic excitation energy calculations. However, calculating the corresponding oscillator strengths is challenging. The corresponding many-electron wavefunctions are not directly accessible. Both the ground-state and the excited-state wave functions from ΔSCF are described by reference Kohn–Sham (KS) single-determinant wavefunctions for the fictitious non-interacting systems. The non-orthogonality between the ground and excited Kohn–Sham determinants from two different SCF calculations leads to unphysically origin-dependent transition properties, such as transition dipole moment and length-gauge oscillator strength. Including nuclei contribution to the perturbation is theoretically rigorous, but its effectiveness is limited only to neutral systems, as we show theoretically and numerically. While several other practical approaches have been proposed to tackle the non-orthogonality problem and yield reasonable results, inevitably, the determinant of the ground state or the excited state is changed, as well as the density matrix. In this work, we explore the use of the velocity gauge to compute oscillator strength within ΔSCF theory. We demonstrate that the velocity gauge is capable of naturally accounting for the non-orthogonality of ΔSCF KS wavefunctions and offering origin-independent predictions without any additional correction schemes to the KS wavefunctions. Compared to the length-gauge results obtained via symmetric orthogonalization, the velocity gauge can offer comparable results. Furthermore, the adoption of spin-purified singlet excitation energy in the velocity-gauge transition dipole moment significantly enhances the overall performance of the velocity gauge for ΔSCF oscillator strength predictions of conjugated chromophores.
R-matrix benchmark study of SO2 photoionization dynamics
Sulfur dioxide is a molecule of broad atmospheric and astronomical relevance, playing a central role in greenhouse warming and ozone chemistry. It is widely detected in planetary and interstellar environments and further participates in sulfur plasma and shock-driven processes. For this purpose, the ab initio R-matrix method within the close-coupling approximation is employed to investigate the photoionization dynamics of sulfur dioxide. Total and state-resolved cross sections for the three lowest ionic states, arising from ionization of the valence orbitals, are calculated, revealing rich autoionizing resonances in the near-threshold region. The high-resolution computed cross sections are benchmarked against available experimental datasets, including both direct measurements and reconstructed data, with the reported partial-channel cross sections representing the first direct state-resolved theoretical results for the dominant photoionization channels. In the absence of fully state-resolved experimental measurements for the cationic states of SO2, the results are interpreted through comparison with fragment-resolved data, enabling a direct correspondence between the computed ionic states and dominant molecular ion production channels, where the lowest three ionic states, X2A1(8a1−1), A2B2(5b2−1), and B2A2(1a2−1), contribute solely to the formation of the parent molecular ion SO2+. To assess the reliability of the results, a series of systematic benchmarks with respect to active space size, basis set, target-state expansion, and R-matrix radius confirms the convergence and robustness of the computed cross sections. The present results provide reliable reference data for modeling photochemical and radiative processes in planetary atmospheres and the interstellar medium and establish SO2 as a benchmark system for molecular photoionization studies.
Development of a recombinant single-cycle influenza viral vector as an intranasal vaccine against SARS-CoV-2
Abstract The COVID-19 pandemic has demonstrated the detrimental potential of zoonotic coronavirus transmission to human populations. Effective vaccines capable of eliciting immunity to SARS-CoV-2 have been pivotal in mitigating the spread of the virus. In this study, we describe the generation of a non-replicating pseudotyped influenza A virus (S-FLU), where the native haemagglutinin (HA) sequence is replaced with the coding sequence of either a membrane-anchored form (TM) or secretory form (Sec) of the receptor-binding domain (RBD) of the ancestral SARS-CoV-2 Wuhan (S-RBD Wuhan). We showed that both S-RBD-TM and S-RBD-Sec viruses can be generated via reverse genetics and grown to high titre. Intranasal immunisation in mice with S-RBD-TM elicits robust serum binding and neutralisation activity against SARS-CoV-2, superior to S-RBD-Sec. Furthermore, we demonstrate that a heterologous prime-boost immunisation regimen in mice with S-RBD-TM Wuhan and S-RBD-TM BM48-31 (a distant Clade 3 SARS-like betacoronavirus (sarbecovirus)) increases antibody binding breadth against mismatched sarbecoviruses compared to homologous prime-boost with S-RBD-TM Wuhan, although this did not translate into significantly enhanced cross-neutralisation across the tested virus panel. These results demonstrate that S-RBD delivery via the intranasal route induces both systemic and mucosal antibody responses and provide a foundation for further optimisation of S-RBD sarbecovirus vaccine strategies.
Impact of Gaussian feathering on diagnostic metrics in tile-based micro-CT sinogram infilling
Abstract Tile-based sinogram infilling methods for micro-CT require merging overlapping output tiles to reconstruct full images. Gaussian feathering, the standard blending approach, produces visually seamless results but its effect on diagnostic image quality metrics has not been characterized. This study quantifies how Gaussian feathering affects noise power spectrum (NPS), modulation transfer function (MTF), noise equivalent quanta (NEQ), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) compared to nearest priority blending, also known as Voronoi partition blending. We mathematically derived the variance reduction mechanism in Gaussian blending and experimentally compared both methods using sinograms infilled by DeepFill v2 as a representative generative inpainting model, applied to a micro-CT quality assurance phantom reconstructed from 50% undersampled data. Gaussian feathering reduced variance by up to 19.6% at overlap centers, causing NPS reduction of up to 13.8% at low spatial frequencies and NEQ inflation of up to 38.5%. MTF showed mixed effects with improvements at low frequencies but reductions at high frequencies. SSIM and PSNR showed statistically significant differences: sinogram PSNR differed by 0.05 dB ( $$p = 0.003$$ ) and reconstruction SSIM by 0.009 ( $$p = 0.036$$ ), both with small effect sizes. In this experimental configuration, Gaussian feathering distorted diagnostic metrics through a variance reduction mechanism, while standard fidelity metrics detected only subtle changes. Nearest priority blending may be preferred when diagnostic metrics are used to validate infilling methods, pending broader empirical validation.
Health promotion and options for digital health interventions on board merchant vessels
Superior synergistic corrosion inhibition of brass in NaCl solution by 2-mercaptobenzothiazole and TiO2 nanoparticles compared with SiO2
Abstract 2-Mercaptobenzothiazole (MBT) combined with nanoparticles exhibits a synergistic corrosion inhibition effect for brass in 3.5% NaCl solution. The corrosion behavior was evaluated using open circuit potential (OCP), electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization techniques. The inhibition efficiency increased with MBT concentration (0–20 ppm), reaching 97.4% at 20 ppm. The addition of 10 ppm nanoparticles further enhanced the inhibition performance. In particular, TiO 2 nanoparticles improved the efficiency to 98.2%, showing superior performance compared with SiO 2 nanoparticles (94%). The adsorption studies showed that the Langmuir isotherm model (R² = 0.9941) was adopted by MBT, with ΔG° ads = (− 12.66 kJ mol −1 ) and K ads = 2.98 L mol −1 , indicating spontaneous adsorption dominated by physical with weak chemical interaction. Density Functional Theory (DFT) calculations at the B3LYP/LANL2DZ level were performed to study the electronic properties and adsorption behavior of MBT and its nanoparticle-modified systems. The results show that nanoparticle incorporation enhances the reactivity and adsorption characteristics of MBT. The MBT–TiO 2 system exhibits the most negative adsorption energy, smaller energy gap, and higher charge transfer ability (ΔN max ), indicating strong interaction with partial chemisorption character, while the MBT–SiO 2 system shows weaker adsorption despite higher electrophilicity. This improvement is attributed to the synergistic interaction between MBT and the incorporated nanoparticles, which enhanced the adsorption strength of inhibitor molecules onto the brass surface and promoted the formation of a highly compact protective layer through effective filling of surface pores and structural imperfections. The originality of this work is demonstrated by the fact that the electronic nature of the nanoparticles, rather than the physical barrier effect, dominates the compactness, interfacial stability and electrochemical performance of the MBT-based protective films.
Deep learning based apple leaf disease detection using spatially modulated continuouslayer
Long-term outcomes after endoscopic resection of gastric adenocarcinoma of the fundic gland type and oxyntic gland adenoma: a retrospective cohort study
Talquetamab–Daratumumab in Relapsed or Refractory Myeloma
Initial data from the prospectively randomized G-MEMBRANE trial and systematic review on the embolization of the middle meningeal artery in the treatment of chronic subdural hematomas
Periodontitis aggravates high-fat diet-induced MASLD via gut microbiota dysbiosis and metabolic dysfunction in mice
Collagen gene expression profiles predict recurrence and progression of DCIS to IDC
One-part slag-mullite geopolymer: role of solid activators on connected porosity and mechanical properties
Visually challenging conditions on sign language intelligibility show behavioural analogies with spoken language
Abstract This study aimed to understand how visually degraded conditions affect the intelligibility of isolated signs in sign language, and how these conditions influence perceived difficulty. Twenty-nine fluent users of Swedish Sign Language viewed 100 isolated signs presented under two types of visual degradation: spectral degradation (pixelation) and background noise (salt-and-pepper noise), each across five levels of degradation. Participants identified each sign and rated the perceived difficulty. Generalized linear mixed models were used to evaluate the effect of level of degradation on intelligibility and perceived difficulty, separately and for each type of degradation. Psychometric functions were estimated, and generalized linear mixed models were used to analyse the relationship between intelligibility and perceived difficulty. Both types of degradation significantly reduced intelligibility and increased perceived difficulty. Psychometric curves showed that intelligibility plateaued around 70% accuracy, mirroring results from speech-in-noise research. Correct responses were consistently rated as easier to perceive, and an interaction between degradation level and response accuracy was observed only in the spectral degradation condition, suggesting distinct perceptual processing mechanisms. Visual degradations affect sign language intelligibility in ways comparable to auditory distortions in speech, with differences between degradation types suggesting distinct perceptual strategies. These findings have implications for theories about linguistic processing in perceptually challenging conditions and call for further investigation into cognitive and linguistic factors that influence sign perception in degraded conditions.
On the smart coordination of flexibility scheduling in multi-carrier integrated energy systems
Abstract Coordinating the interactions among flexibility assets in multi-carrier integrated energy systems (MIES) can lead to a cost-efficient energy transition. However, the proliferation of flexibility assets and their growing participation in active demand response increases the complexity of coordinating these interactions. This paper investigates several approaches to model the coordination of flexibility scheduling in MIES with many autonomous flexibility providers. We propose runtime model coupling as an alternative modeling paradigm to overcome the limitations of monolithic centralized co-optimization. Specifically, we introduced two model coupling approaches—a distributed price-response and a decentralized market auction approach—to address practical challenges such as preserving the autonomy and privacy of flexibility providers while ensuring scalability. We conduct a quantitative benchmark of these approaches against co-optimization across varying problem sizes, complexities, and computing infrastructures. This benchmark provides new empirical insights into the trade-offs between optimality, autonomy, and scalability that have so far remained unquantified in the energy system literature. We show that model coupling offers a method to balance optimality and realism (autonomy and privacy) while delivering substantial scalability gains. The proposed model coupling approaches are formalized as open-source software with several practical applications: modelers can experiment with different flexibility modeling approaches and choose the one that best matches their modeling objectives and constraints; flexibility providers can couple their models to simulate interactions between their systems to make informed operational decisions without disclosing any confidential information.