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Abstract We study the competition between the p-wave and the p+ip superfluid solutions in a holographic model with applied magnetic field intensity H. We find that when H is turned on, both the grand potential and the critical temperature of the p+ip solution are shifted, while the p-wave solution is only slightly affected. Combining the effect of H and back reaction parameter b, we build H − T phase diagrams with a slit region of p+ip phase. The zero (or finite) value of H at the starting point of the slit region is related to second (or first) order of the p-wave phase transition at zero magnetic intensity, which should be universal in systems with degenerate critical points (spinodal points) at zero magnetic field.

Digital technologies are playing an increasing role in the global response to tuberculosis (TB), however their effectiveness and impact are often shaped in the context in which they are implemented. Implementation research can help facilitate the effective introduction of digital health technologies in TB programmes. In 2020, the Implementation Research for Digital Technologies and TB online toolkit (IR4DTB) was developed and launched by the Special Programme for Research and Training in Tropical Diseases, and the Global TB Programme at the World Health Organization (WHO), to build local capacity for IR and promote the effective use of digital technologies within TB programmes. This paper describes the development and piloting of the IR4DTB toolkit, a self-learning tool designed for TB programme implementers. The toolkit comprises six modules reflecting key steps of the IR process, practical instructions and guidance on how to complete these steps, and real-word case studies to illustrate key learning points. This paper also describes the launch of the IR4DTB during a five-day training workshop with TB staff from China, Uzbekistan, Pakistan, Malaysia. The workshop included facilitated sessions on the IR4DTB modules, and provided an opportunity for participants to work with facilitators to develop a comprehensive IR proposal addressing an identified challenge related to the implementation and/or scale-up of digital health technologies for TB care in their home country. Post-workshop evaluation revealed high level of satisfaction among participants with the workshop content and format. The IR4DTB toolkit is a replicable model which can be used to strengthen the TB staff capacity to innovate within a culture of continuous collection of evidence. Through continued trainings and adaptation of the toolkit alongside the integration of digital technologies within TB prevention and care, this model has the potential to contribute directly to all components of the End TB Strategy.

C23H18FeO, orthorhombic, Pna21 (no. 33), a = 28.3217(11) Å, b = 5.7477(2) Å, c = 10.3071(3) Å, β = 90°, V = 1677.84(10) Å3, Z = 4, Rgt(F) = 0.0372, wRref(F2) = 0.0713, T = 150 K.

Software defined network (SDN) is a new network architecture proposed by the clean slate research group of Stanford University.The significant feature of this architecture is to decouple the functions of control plane and forwarding plane,and to flexibly forward the network traffic.Based on this,Internet service providers have deployed SDN technology in their backbone network to maximize the utilization of network resources.However,due to the limitation of economic cost and technical conditions,the backbone network of internet service providers must be in the hybrid SDN network for a long time.The studies have shown that single network node failure is inevitable and occurs frequently.Therefore,it is a key scientific problem to study the routing protection me-thod for single network component failure in hybrid SDN networks.In this paper,the route protection method for single network component failure in hybrid SDN network is described,and then two heuristic methods are used to solve the problem.Finally,the proposed heuristic algorithms are tested in real and simulated topologies.The experimental results show that in the traditional backbone network,only a part of the traditional devices need to be upgraded to SDN devices,and the algorithms proposed in this paper can deal with all possible single network node failure cases in the network.

Sequestering CO2 in depleted oil reservoirs provides one of the most appealing measures to reduce greenhouse gases (GHG) concentration in the atmosphere. The remaining liquids after enhanced oil recovery (EOR) processes, including residual oil and remaining water, lead to the main challenges to this approach. How to effectively evacuate a depleted oil reservoir by recovering not only residual oil but also remaining water is a critical consideration for this type of CO2 sequestration. This paper presents conceptual investigations concerning the methods which effectively evacuate depleted oil reservoirs from both the displacement efficiency and the sweep efficiency points of view. To improve the displacement efficiency, surfactant slug and solvent slug injection was examined using a core scale numerical model. Investigations regarding improving sweep efficiency, such as horizontal well pattern infilling and foam injection, were carried out based on a typical row well pattern. Simulation results showed that surfactant slug which modified the relative permeability and capillary pressure remarkably reduced both residual oil saturation and remaining water saturation. A CO2 slug injected before surfactant slug can help improve the oil recovery. Solvent enriched CO2 slug also remarkably reduced the residual oil saturation to as low as 2%. Horizontal well pattern infilling had great advantage for thick or inclined reservoirs, and foam slug injection greatly improved CO2 storage capacity in thin reservoirs by improving the sweep efficiency. Maximum mobility reduction (MRF) is the most important parameter to maximize the storage capacity and the benefit. The variation of CO2 storage capacity along with CO2 slug size. Larger foam slug size will play a better storage performance. The conceptual simulation investigations confirmed that depleted oil reservoirs can be effectively evacuated for CO2 storage. Depleted oil reservoirs with maximum evacuation are the best candidates for CO2 sequestrations. [ABSTRACT FROM AUTHOR]

The public health issues caused by low temperature have led to urgent demands on high-performance warmth retention materials to prevent heat loss of human body. Fiber materials, the most common warmth retention materials, always suffer from large weight, flammability and poor heat retention capacity. Here, we propose an effective strategy to fabricate flame-retardant and ultralight fibrous sponges (FUFSs) with mechanical robustness and remarkable warmth retention performance by humidity-induced electrospinning technology. Precisely tailoring the relative humidity leads to the construction of ultrafine fibrous sponges, while the flame retardance is achieved by introducing flame retardant. The resultant FUFSs exhibit ultralight property (density of 3.1 mg cm−3), mechanical robustness (∼0% plastic deformations after 1000 stretchable and compressive tests), desirable warmth retention performance (thermal conductivity of 23.3 mW m−1 K−1), and flame retardance (limit oxygen index of 25.2%). This work may open a new way for developing mechanically robust and flame-retardant three-dimensional fibrous materials for various applications. [ABSTRACT FROM AUTHOR]

Seismic deconvolution is a useful tool in seismic data processing. Classical non-machine learning deconvolution methods usually apply quite a few constraints to both wavelet inversion and reflectivity inversion. Supervised machine learning deconvolution methods often require appropriate training labels. The existing self-supervised machine learning deconvolution methods need a given wavelet, which is a non-blind process. To overcome these issues, we propose a blind deconvolution method based on self-supervised machine learning. This method first estimates an initial zero-phase wavelet by smoothing the amplitude spectrum of averaged seismic data. Then, the loss function of self-supervised machine learning is taken as the error between the observed seismic data and the reconstructed seismic data that come from the convolution of phase-rotated wavelet and reflectivity generated by the network. We utilize a residual neural network with long skip connections as the reflectivity inversion network and a fully connected convolutional neural network as the wavelet phase inversion network. Numerical experiments on synthetic data and field data show that the proposed method can obtain reflectivity inversion results with higher resolution than the existing self-supervised machine learning method without given wavelet. [ABSTRACT FROM AUTHOR]

Hyperglycemia is a crucial risk factor for cardiovascular diseases. Chronic inflammation is a central characteristic of obesity, leading to many of its complications. Recent studies have shown that high glucose activates Yes-associated protein 1 (YAP) by suppressing AMPK activity in breast cancer cells. Metformin is a commonly prescribed anti-diabetic drug best known for its AMPK-activating effect. However, the role of YAP in the vasoprotective effect of metformin in diabetic endothelial cell dysfunction is still unknown. The present study aimed to investigate whether YAP activation plays a role in obesity-associated endothelial dysfunction and inflammation and examine whether the vasoprotective effect of metformin is related to YAP inhibition. Reanalysis of the clinical sequencing data revealed YAP signaling, and the YAP target genes CTGF and CYR61 were upregulated in aortic endothelial cells and retinal fibrovascular membranes from diabetic patients. YAP overexpression impaired endothelium-dependent relaxations (EDRs) in isolated mouse aortas and increased the expression of YAP target genes and inflammatory markers in human umbilical vein endothelial cells (HUVECs). High glucose-activated YAP in HUVECs and aortas was accompanied by increased production of oxygen-reactive species. AMPK inhibition was found to induce YAP activation, resulting in increased JNK activity. Metformin activated AMPK and promoted YAP phosphorylation, ultimately improving EDRs and suppressing the JNK activity. Targeting the AMPK–YAP–JNK axis could become a therapeutic strategy for alleviating vascular dysfunction in obesity and diabetes.

Abstract Introduction There are few analyses of the 15 red blood group system antigen coding genes found in the Yunnan Yi nationality. This has caused many poteintial dangers relating to clinical blood transfusion. In this report, the coding genes and distribution of 15 blood group antigens system in the Yi nationality were tested and compared with those of Han nationality and other ethnic minorities. Methods The samples came from the healthy subjects in the first people's Hospital of Qujing, Yunnan Province. Two hundred and three Yunnan Yi and 197 Han nationality individuals were included. Thirty‐three blood group antigens with a low frequency from the 15 blood group systems of Yunnan Yi blood donors were genotyped and analyzed by PCR‐SSP. Sanger sequencing was used to detect A4GALT from the Yunnan Yi nationality. The χ2 test was used to analyze observed and expected values of gene distribution to verify conformation to the Hardy‐Weinberg equilibrium law. Fisher's exact test was used to analyze gene frequency distribution, and the statistical significance was set at p 0.05). Compared with Tibetan (Tibet), Zhuang (Nanning), and Dong (Guangxi), the gene distribution frequencies of Rh blood group system phenotype CC were significantly lower in the Yunnan Yi nationality (p