A
bstract
We study Ising Field Theory (the scaling limit of Ising model near the Curie critical point) in pure imaginary external magnetic field. We put particular emphasis on the detailed structure ...of the Yang-Lee edge singularity. While the leading singular behavior is controlled by the Yang-Lee fixed point (= minimal CFT
M
2
/
5
), the fine structure of the subleading singular terms is determined by the effective action which involves a tower of irrelevant operators. We use numerical data obtained through the “Truncated Free Fermion Space Approach” to estimate the couplings associated with two least irrelevant operators. One is the operator
T
T
¯
, and we use the universal properties of the
T
T
¯
deformation to fix the contributions of higher orders in the corresponding coupling parameter
α
. Another irrelevant operator we deal with is the descendant
L_
4
L
¯
_
4
ϕ
of the relevant primary
ϕ
in
M
2
/
5
. The significance of this operator is that it is the lowest dimension operator which breaks integrability of the effective theory. We also establish analytic properties of the particle mass
M
(= inverse correlation length) as the function of complex magnetic field.
•Effects of berberine and metformin were evaluated in db/db obese T2DM mice.•Both berberine and metformin reduced food intake and promoted weight loss.•Both treatments reduced blood glucose, HbA1c, ...and LPS levels.•The drugs relieved intestinal inflammation and repaired intestinal barrier structure.•The effects of berberine and metformin are exerted via the gut microbiome.
Berberine and metformin, both established pharmaceutical agents with herbal origins, have incidental beneficial effects on multiple diseases, including diabetes. These effects have been speculated to occur via the gut microbiome. In this study, we administered either berberine or metformin to db/db mice and investigated changes in body weight, food intake, and blood glucose levels. Fresh stool samples were analyzed using 16 s rDNA high-throughput sequencing to evaluate the gut microbiome. Short-chain fatty acids (SCFA) in the stool were quantified using gas chromatography. The expression of NF-κB signaling pathway and tight junction (ZO1 and occludin) proteins in the intestinal epithelium was determined using qPCR and western blotting. The intestinal barrier structure was examined using transmission electron microscopy and serum lipopolysaccharide (LPS) was measured using a commercial kit. Both berberine and metformin reduced food intake, body weight, and blood glucose and HbA1c levels. Both treatments effectively restored the intestinal SCFA content, reduced the level of serum LPS, relieved intestinal inflammation, and repaired intestinal barrier structure. Intervention with metformin or berberine modified the gut microbiome in db/db mice, increasing the number of SCFA-producing bacteria (e.g., Butyricimonas, Coprococcus, Ruminococcus) and reducing opportunistic pathogens (e.g., Prevotella, Proteus). An increased abundance of other probiotics including Lactobacillus and Akkermansia was also observed. Berberine and metformin can modulate the composition of the gut microbiome and reduce body weight, blood glucose levels, and intestinal inflammation in db/db mice, which demonstrates their effectiveness in the reduction of diabetic complications in this model.
In this paper, we investigate the hydrodynamic collectivity in proton–proton (p–p) collisions at 13 TeV, using iEBE-VISHNU hybrid model with HIJING initial conditions. With properly tuned parameters, ...our model simulations can remarkably describe all the measured 2-particle correlations, including integrated and differential elliptic flow coefficients for all charged and identified hadrons (KS0, Λ). However, our model calculations show positive 4-particle cumulant c2{4} in high multiplicity pp collisions, and can not reproduce the negative c2{4} measured in experiment. Further investigations on the HIJING initial conditions show that the fluctuations of the second order anisotropy coefficient ε2 increases with the increase of its mean value, which leads to a similar trend of the flow fluctuations. For a simultaneous description of the 2- and 4- particle cumulants within the hydrodynamic framework, it is required to have significant improvements on initial condition for pp collisions, which is still lacking of knowledge at the moment.
•Remotely sensed metrics are effective for monitoring vegetation responses to meteorological drought.•Drought resistance is associated with water balance and vegetation characteristics.•Drought ...impacts are determined by water stress levels and drought resistance among ecosystems.•Arid and semi-arid ecosystems are most susceptible to drought.•Future drought may threaten the survival of mesic ecosystems.
Improving our understanding of present and future impacts of drought on the vegetation in northern China is heightened by expectations that drought would increase its vulnerability and subsequently accelerate land degradation. The response of vegetation activity to drought and the underlying mechanisms are not well known. By using the third-generation Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation Evapotranspiration Index (SPEI), we investigated the relationship between NDVI and SPEI, across different climate regimes and land cover types, and determined the dominant time-scales at which different biome types respond to drought during the period of 1981–2014. Our results showed that biome response is coupled with drought trends in most regions of northern China. The highest correlation between monthly NDVI and SPEI at different time scales (1–48 months) assessed the impact of drought on vegetation, and the time scales resulting in the highest correlation were an effective indicator of drought resistance, which was related to the interactive roles of mean water balance and divergent drought survival traits and strategies. Diverse responses of vegetation to drought were critically dependent on characteristic drought time-scales and different growing environments. This study highlighted the most susceptible ecosystem types to drought occurrence under current climate, including temperate steppes, temperate desert steppes, warm shrubs and dry forests. Given that drought will be more frequent and severe under future climate scenarios, it may threaten the survival of mesic ecosystems, such as temperate meadows, alpine grasslands, dwarf shrubs, and moist forests not normally considered at drought risk. We propose that future research should be focused on arid and semi-arid ecosystems, where the strongest impact of drought on vegetation is occurring and the need for an early warning drought system is increasingly urgent.
The stochastic optimal control of nonlinear networked control systems (NNCSs) using neuro-dynamic programming (NDP) over a finite time horizon is a challenging problem due to terminal constraints, ...system uncertainties, and unknown network imperfections, such as network-induced delays and packet losses. Since the traditional iteration or time-based infinite horizon NDP schemes are unsuitable for NNCS with terminal constraints, a novel time-based NDP scheme is developed to solve finite horizon optimal control of NNCS by mitigating the above-mentioned challenges. First, an online neural network (NN) identifier is introduced to approximate the control coefficient matrix that is subsequently utilized in conjunction with the critic and actor NNs to determine a time-based stochastic optimal control input over finite horizon in a forward-in-time and online manner. Eventually, Lyapunov theory is used to show that all closed-loop signals and NN weights are uniformly ultimately bounded with ultimate bounds being a function of initial conditions and final time. Moreover, the approximated control input converges close to optimal value within finite time. The simulation results are included to show the effectiveness of the proposed scheme.
Nowadays, the Industrial Internet of Things (IIoT) has remarkably transformed our personal lifestyles and society operations into a novel digital mode, which brings tremendous associations with all ...walks of life, such as intelligent logistics, smart grid, and smart city. Moreover, with the rapid increase of IIoT devices, a large amount of data is swapped between heterogeneous sensors and devices every moment. This trend increases the risk of eavesdropping and hijacking attacks in communication channels, so maintaining data privacy and security becomes two notable concerns at present. Recently, based on the mechanism of the Schnorr signature, a more secure and lightweight certificateless signature (CLS) protocol is popular for the resource-constrained IIoT protocol design. Nevertheless, we found most of the existing CLS schemes are susceptible to several common security weaknesses such as man-in-the-middle attacks, key generation center compromised attacks, and distributed denial of service attacks. To tackle the challenges mentioned previously, in this article, we propose a novel pairing-free certificateless scheme that utilizes the state-of-the-art blockchain technique and smart contract to construct a novel reliable and efficient CLS scheme. Then, we simulate the Type-I and Type-II adversaries to verify the trustworthiness of our scheme. Security analysis as well as performance evaluation outcomes prove that our design can hold more reliable security assurance with less computation cost (i.e., reduced by around 40.0% at most) and communication cost (i.e., reduced by around 94.7% at most) than other related schemes.
Single-pixel imaging uses a single-pixel detector, rather than a focal plane detector array, to image a scene. It provides advantages for applications such as multi-wavelength, three-dimensional ...imaging. However, low frame rates have been a major obstacle inhibiting the use of computational ghost imaging technique in wider applications since its invention one decade ago. To address this problem, a computational ghost imaging scheme, which utilizes an LED-based, high-speed illumination module is presented in this work. At 32 × 32 pixel resolution, the proof-of-principle system achieved continuous imaging with 1000 fps frame rate, approximately two orders larger than those of other existing ghost imaging systems. The proposed scheme provides a cost-effective and high-speed imaging technique for dynamic imaging applications.
Secretion of misfolded tau, a microtubule-binding protein enriched in nerve cells, is linked to the progression of tau pathology. However, the molecular mechanisms underlying tau secretion are poorly ...understood. Recent work by Lee et al. Biochemical J. (2021) 478: 1471-1484 demonstrated that the transmembrane domains of syntaxin6 and syntaxin8 could be exploited for tau release, setting a stage for testing a novel hypothesis that has profound implications in tauopathies (e.g. Alzheimer's disease, FTDP-17, and CBD/PSP) and other related neurodegenerative diseases. The present commentary highlights the importance and limitations of the study, and discusses opportunities and directions for future investigations.
The stochastic optimal controller design for the nonlinear networked control system (NNCS) with uncertain system dynamics is a challenging problem due to the presence of both system nonlinearities ...and communication network imperfections, such as random delays and packet losses, which are not unknown a priori. In the recent literature, neuro dynamic programming (NDP) techniques, based on value and policy iterations, have been widely reported to solve the optimal control of general affine nonlinear systems. However, for realtime control, value and policy iterations-based methodology are not suitable and time-based NDP techniques are preferred. In addition, output feedback-based controller designs are preferred for implementation. Therefore, in this paper, a novel NNCS representation incorporating the system uncertainties and network imperfections is introduced first by using input and output measurements for facilitating output feedback. Then, an online neural network (NN) identifier is introduced to estimate the control coefficient matrix, which is subsequently utilized for the controller design. Subsequently, the critic and action NNs are employed along with the NN identifier to determine the forward-in-time, time-based stochastic optimal control of NNCS without using value and policy iterations. Here, the value function and control inputs are updated once a sampling instant. By using novel NN weight update laws, Lyapunov theory is used to show that all the closed-loop signals and NN weights are uniformly ultimately bounded in the mean while the approximated control input converges close to its target value with time. Simulation results are included to show the effectiveness of the proposed scheme.
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