Explore the vast range of titles available.

On the one hand, Andriot and Roupec (Fortsch Phys, 1800105, 2019) proposed an alternative refined de Sitter conjecture, which gives a natural condition on a combination of the first and second derivatives of the scalar potential (Andriot and Roupec 2019). On the other hand, in our previous article (Liu in Eur Phys J Plus 136:901, 2021) , we have found that Palatini Higgs inflation model is in strong tension with the refined de Sitter swampland conjecture (Liu 2021). Therefore, following our previous research, in this article we examine if Higgs inflation model and its two variations: Palatini Higgs inflation and Higgs-Dilaton model (Rubio in Front Astron Space Sci, https://doi.org/10.3389/fspas.2018.00050, 2019) can satisfy the “further refining de Sitter swampland conjecture” or not. Based on observational data (Ade et al., Phys Rev Lett 121:221301, 2018; Akrami et al., Planck 2018 results. X. Constraints on inflation, arXiv:1807.06211 [astro-ph.CO], 2018; Aghanim et al., Planck 2018 results: VI. Cosmological parameters, arXiv:1807.06209 [astro-ph.CO], 2018), we find that these three inflationary models can always satisfy this new swampland conjecture if only we adjust the relevant parameters a, b=1-a and q. Therefore, if the “further refining de Sitter swampland conjecture” does indeed hold, then the three inflationary models might all be in “landscape”.

In this article, we intend to find a specific model which can satisfy the further refining dS swampland conjecture and scalar weak gravity conjecture (SWGC) simultaneously, in particular, Higgs inflation model and its two extensions: Higgs-dilaton model and Palatini Higgs inflation. We determine the conditions if the three inflation models satisfy scalar weak gravity conjecture (SWGC) and strong scalar weak gravity conjecture (SSWGC).

A bstract We demonstrate a unified resolution to the strong CP, hierarchy, and cosmological constant problems in type IIA flux compactifications, via 4-form fluxes and KL stabilization. We show that the strong CP problem can be effectively “solved” in type IIA orientifold constructions, particularly in the type IIA T 6 /( ℤ 2  ×  ℤ 2 ) model. Building on this, we explore whether the remaining two fine-tuning problems can also be resolved within the same setup. To obtain a small cosmological constant, we adopt the KL scenario and find that, in order to avoid conflicts with the swampland distance conjecture and to eliminate the need for fine-tuning, the perturbative superpotential ∆ W must take the form f 0 U 3 . Additionally, we compute the gravitino mass. This allows for a resolution of the hierarchy problem without introducing fine-tuning if gravitino mass lies below 100 TeV. Taken together, these results suggest that the type IIA T 6 /( ℤ 2  ×  ℤ 2 ) orientifold model provides a promising framework in which all three fine-tuning problems may be addressed simultaneously.

Research on biomarker-driven therapy and immune check-point blockade in non-small cell lung cancer (NSCLC) is rapidly evolving. The width and depth of clinical trials have also dramatically improved in an unprecedented speed. The personalized treatment paradigm evolved every year. In this review, we summarize the promising agents that have shifted the treatment paradigm for NSCLC patients across all stages, including targeted therapy and immunotherapy using checkpoint inhibitors. Based on recent evidence, we propose treatment algorithms for NSCLC and propose several unsolved clinical issues, which are being explored in ongoing clinical trials. The results of these trials are likely to impact future clinical practice.

We employ a ghost model of interacting dark energy to obtain the equation of state ω for ghost energy density in an FRW universe in complex quintessence theory. We reconstruct the potential and study the dynamics of the scalar field that describes complex quintessence cosmology. We perform ω - ω ′ analysis and stability analysis for both non-interacting and interacting cases and find that the same basic conclusion as for the real model, where ω ′ = d ω / d l n a . Taking account of the effect of the complex part and assuming the real part of the quintessence field to be a slow-rolling field, we conclude that the non-interacting model cannot describe the real universe since this will lead to fractional energy density Ω D > 1 , where Ω D can be defined as the ratio of ρ D to ρ cr . However, for the interacting case, if we take present Ω D = 0.73 , then we can determine that b 2 = 0.0849 , where b 2 is the interaction coupling parameter between matter and dark energy. In the real quintessence model, Ω D and b 2 are independent parameters, whereas in the complex quintessence model, we conclude that there is a relationship between these two parameters.

Hawking radiation is one of the cores in modern gravitational theory. Several articles have calculated the spectrum of Hawking radiation in Boltzmann–Gibbs statistical mechanics. However, based on recent researches, gravitational systems cannot be studied by the standard statistical mechanics. In this article, we calculate the modification to the spectrum of Hawking radiation in Tsallis statistical mechanics. We obtain the modified Stefan–Boltzmann’s law and modified power of Hawking radiation. We confirm the conclusion proposed by Giddings, namely, the radiation should originate from the effective radius, which extends well outside the horizon of black-hole. The lifetime of black hole and the effect of large q are discussed as well.

Carbon dots (CDs) are photoluminescent nanomaterials with wide-ranging applications. Despite their photoactivity, it remains unknown whether CDs degrade under illumination and whether such photodegradation poses any cytotoxic effects. Here, we show laboratory-synthesized CDs irradiated with light degrade into molecules that are toxic to both normal (HEK-293) and cancerous (HeLa and HepG2) human cells. Eight days of irradiation photolyzes 28.6-59.8% of the CDs to <3 kilo Dalton molecules, 1431 of which are detected by high-throughput, non-target high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Molecular network and community analysis further reveal 499 cytotoxicity-related molecules, 212 of which contain polyethylene glycol, glucose, or benzene-related structures. Photo-induced production of hydroxyl and alkyl radicals play important roles in CD degradation as affected by temperature, pH, light intensity and wavelength. Commercial CDs show similar photodegraded products and cytotoxicity profiles, demonstrating that photodegradation-induced cytotoxicity is likely common to CDs regardless of their chemical composition. Our results highlight the importance of light in cytocompatibility studies of CDs. Carbon dots have attracted much attention for biomedical applications but potential degradation and associated toxicity are still poorly understood. Here, the authors report on a study into the photo-degradation of carbon dots, the products produced and associated cytotoxicity.

The digital economy is an important engine to promote sustainable economic growth. Exploring the mechanism by which the digital economy promotes economic development, industrial upgrading and environmental improvement is an issue worth studying. This paper takes China as an example for study and uses the data of 286 cities from 2011 to 2019. In the empirical analysis, the direction distance function (DDF) and the Global Malmquist-Luenberger (GML) productivity index methods are used to measure the green total factor productivity (GTFP), while Tobit, quantile regression, impulse response function and intermediary effect models are used to study the relationship among digital economy development, industrial structure upgrading and GTFP. The results show that: (1) The digital economy can significantly improve China’s GTFP; however, there are clear regional differences. (2) The higher the GTFP, the greater the promotion effect of the digital economy on the city’s GTFP. (3) From a dynamic long-term perspective, the digital economy has indeed positively promoted China’s GTFP. (4) The upgrading of industrial structures is an intermediary transmission mechanism for the digital economy to promote GTFP. This paper provides a good reference for driving green economic growth and promoting the environment.

Harnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and understanding of such systems in various science and engineering disciplines. This work introduces a novel approach called physics-informed neural network with sparse regression to discover governing partial differential equations from scarce and noisy data for nonlinear spatiotemporal systems. In particular, this discovery approach seamlessly integrates the strengths of deep neural networks for rich representation learning, physics embedding, automatic differentiation and sparse regression to approximate the solution of system variables, compute essential derivatives, as well as identify the key derivative terms and parameters that form the structure and explicit expression of the equations. The efficacy and robustness of this method are demonstrated, both numerically and experimentally, on discovering a variety of partial differential equation systems with different levels of data scarcity and noise accounting for different initial/boundary conditions. The resulting computational framework shows the potential for closed-form model discovery in practical applications where large and accurate datasets are intractable to capture. Recovery of underlying governing laws or equations describing the evolution of complex systems from data can be challenging if dataset is damaged or incomplete. The authors propose a learning approach which allows to discover governing partial differential equations from scarce and noisy data.

The gear cracks of gear box are one of most common failure forms affecting gear shaft drive. It has become significant for practice and economy to diagnose the situation of gearbox rapidly and accurately. The extracted signal is filtered first to eliminate noise, which is pretreated for the diagnostic classification based on the particle filter of radial basis function. As traditional error back-propagation of wavelet neural network with falling into local minimum easily, slow convergence speed and other shortcomings, the particle swarm optimization algorithm is proposed in this paper. This particle swarm algorithm that optimizes the weight values of wavelet neural network (scale factor) and threshold value (the translation factor) was developed to reduce the iteration times and improve the convergence precision and rapidity so that the various parameters of wavelet neural network can be chosen adaptively. Experimental results demonstrate that the proposed method can accurately and quickly identify the damage situation of the gear crack, which is more robust than traditional back-propagation algorithm. It provides guidances and references for the maintenance of the gear drive system schemes.