Phenothiazine-based covalent organic frameworks with low exciton binding energies for photocatalysis

Designing delocalized excitons with low binding energy (E_b) in organic semiconductors is urgently required for efficient photochemistry because the excitons in most organic materials are localized with a high E_b of >300 meV. In this work, we report the achievement of a low E_b of ∼50 meV by constructing phenothiazine-based covalent organic frameworks (COFs) with inherent crystallinity, porosity, chemical robustness, and feasibility of bandgap engineering. The low E_b facilitates effective exciton dissociation and thus promotes photocatalysis by using these COFs. As a demonstration, we subject these COFs to photocatalytic polymerization to synthesize polymers with remarkably high molecular weight without any requirement of the metal catalyst. Our results can facilitate the rational design of porous materials with low E_b for efficient photocatalysis.

We report the construction of phenothiazine-based covalent organic frameworks, which exhibited diverse structures, the feasibility of bandgap engineering, and unprecedented ultralow exciton binding energy of ∼50 meV for photocatalytic polymerization.     

A Novel Method for Pattern Recognition of GIS Partial Discharge via Multi-Information Ensemble Learning

Partial discharge (PD) is the main feature that effectively reflects the internal insulation defects of gas-insulated switchgear (GIS). It is of great significance to diagnose the types of insulation faults by recognizing PD to ensure the normal operation of GIS. However, the traditional diagnosis method based on single feature information analysis has a low recognition accuracy of PD, and there are great differences in the diagnosis effect of various insulation defects. To make the most of the rich insulation state information contained in PD, we propose a novel multi-information ensemble learning for PD pattern recognition. First, the ultra-high frequency and ultrasonic data of PD under four typical defects of GIS are obtained through experiment. Then the deep residual convolution neural network is used to automatically extract discriminative features. Finally, multi-information ensemble learning is used to classify PD types at the decision level, which can complement the shortcomings of the independent recognition of the two types of feature information and has higher accuracy and reliability. Experiments show that the accuracy of the proposed method can reach 97.500%, which greatly improves the diagnosis accuracy of various insulation defects.     

A Bregman-Split-Based Compressive Sensing Method for Dynamic Harmonic Estimation

In order to overcome the spectral interference of the conventional Fourier transform in the International Electrotechnical Commission framework, this paper introduces a Bregman-split-based compressive sensing (BSCS) method to estimate the Taylor–Fourier coefficients in a multi-frequency dynamic phasor model. Considering the DDC component estimation, this paper transforms the phasor problem into a compressive sensing model based on the regularity and sparsity of the dynamic harmonic signal distribution. It then derives an optimized hybrid regularization algorithm with the Bregman split method to reconstruct the dynamic phasor estimation. The accuracy of the model was verified by using the cross entropy to measure the distribution differences of values. Composite tests derived from the dynamic phasor test conditions were then used to verify the potentialities of the BSCS method. Simulation results show that the algorithm can alleviate the impact of dynamic signals on phasor estimation and significantly improve the estimation accuracy, which provides a theoretical basis for P-class phasor measurement units (PMUs).     

HIGH-FREQUENCY VIBRATIONS OF CORRUGATED CYLINDRICAL PIEZOELECTRIC SHELLS

Coupled extensional and flexural cylindrical vibrations of a corrugated cylindrical piezoelectric shell consisting of multiple pieces of circular cylindrical surfaces smoothly connected along their generatrix are studied. To validate the results for the case of relatively thick shells or equivalently high-frequency modes with short wavelengths, existing analysis is extended by considering shear deformation and rotatory inertia. An analytical solution is obtained. Based on the solution, resonant frequencies and mode shapes are calculated.     

Quantum Circuit Optimization for Solving Discrete Logarithm of Binary Elliptic Curves Obeying the Nearest-Neighbor Constrained

In this paper, we consider the optimization of the quantum circuit for discrete logarithm of binary elliptic curves under a constrained connectivity, focusing on the resource expenditure and the optimal design for quantum operations such as the addition, binary shift, multiplication, squaring, inversion, and division included in the point addition on binary elliptic curves. Based on the space-efficient quantum Karatsuba multiplication, the number of CNOTs in the circuits of inversion and division has been reduced with the help of the Steiner tree problem reduction. The optimized size of the CNOTs is related to the minimum degree of the connected graph.     

Development of a Quantitative Method for Detection of Multiclass Veterinary Drugs in Feed Using Modified QuPPe Extraction and LC–MS/MS

A method was developed for the rapid and quantitative analysis of 30 veterinary drugs belonging to 17 classes (amphenicols (1), anthelmintics (1), cephalosporins (4), coccidiostats (1), lincosamides (1), macrolide (1), nitroimidazole (1), penicillins (3), phenylhydrazines (1), polypeptides (1), pyrethrins (1), quinolones (5), sulfonamides (3), tetracycline (3), neuroleptic agents (1), triazene trypanocidal agents (1), other. (1)) in feeds. The proposed method with a modified Quick Polar Pesticides (QuPPe) sample preparation was validated for the determination of 30 veterinary drugs in feed samples by liquid chromatography triple-quadrupole mass spectrometry (LC–MS/MS). The sample was extracted with methanol containing 1% acetic acid and purified by dispersive solid-phase extraction (d-SPE) with C18. Good linearity (r² ≥ 0.98) was observed, and the LOQ values ranged from 10 to 200 µg/kg. Average recoveries ranged from 70.8 to 118.4%, and the relative standard deviation was ≤ 18.7%. This validated method was used in the determination of 30 veterinary drugs in 142 feed samples obtained from South Korea. The results show that lincomycin was present in only one of the tested feed samples, although it was detected at a value lower than the LOQ. In conclusion, this multi-residue method can be used for screening through the detection and quantitation of residual multiclass veterinary drugs in feed samples.     

Numerical Study on Entropy Generation of the Multi-Stage Centrifugal Pump

The energy loss of the multi-stage centrifugal pump was investigated by numerical analysis using the entropy generation method with the RNG k-ε turbulence model. Entropy generation due to time-averaged motion and velocity fluctuation was mainly considered. It was found that the entropy generation of guide vanes and impellers account for 71.2% and 23.3% of the total entropy generation under the designed flow condition. The guide vanes are the main hydraulic loss domains and their entropy generation is about 9 W/K, followed by impellers. There are vortices at the tongue of the guide vane inlet as well as flow separations in the impellers, which lead to entropy generation. The fluid impacts the outer surface of the guide vanes, resulting in the increase in entropy generation. There are refluxes near the guide vane tongues which also increase the entropy generation of this part. The entropy generation distribution of the guide vanes and impellers was investigated, which found that the positive guide vane has more entropy generation compared with the reverse guide. The entropy generation of the blade suction surface is higher compared with the pressure surface. This study indicated that the entropy generation method has distinct advantages in the assessment of hydraulic loss.     

Dynamic synovial fibroblasts are modulated by NBCn1 as a potential target in rheumatoid arthritis

Rheumatoid arthritis (RA) is an autoimmune disease characterized by aggressive fibroblast-like synoviocytes (FLSs) and pannus formation. Various therapeutic strategies have been developed against inflammatory cytokines in RA in recent decades. Based on the migratory features of FLSs, we examined whether modulation of the migratory module attenuates RA severity. In this study, inflamed synovial fluid-stimulated FLSs exhibited enhanced migration and migratory apparatus expression, and sodium bicarbonate cotransporter n1 (NBCn1) was identified in primary cultured RA-FLSs for the first time. The NBC inhibitor S0859 attenuated the migration of FLSs induced with synovial fluid from patients with RA or with TNF-α stimulation. Inhibition of NBCs with S0859 in a collagen-induced arthritis (CIA) mouse model reduced joint swelling and destruction without blood, hepatic, or renal toxicity. Primary FLSs isolated from the CIA-induced mouse model also showed reduced migration in the presence of S0859. Our results suggest that inflammatory mediators in synovial fluid, including TNF-α, recruit NBCn1 to the plasma membrane of FLSs to provide dynamic properties and that modulation of NBCn1 could be developed into a therapeutic strategy for RA.Subject terms: Chemotaxis, Bone, Ion channel signalling, Rheumatoid arthritis, Drug development     

文学：概念建构与娱乐主题的沉浮

现代文学概念并非与生俱来,而是现代性理论运作的产物。现代文学概念的定型至少要完成四个方面的理论建构：不同知识体系对接带来的理论扩展,经世致用思想传统的承传,文学语言问题的再认识,叙事文类进入文学视野的中心。相对于现代文学概念的标准涵义,娱乐是一个倍受压抑的主题。尽管遭到了启蒙与革命两种文学观念的强大抵制,但是,作为审美愉悦的一个组成部分,娱乐主题顽强地存留于大众之中。20世纪80年代之后,娱乐主题在“文化产业”的名义下声势浩大地回归,并且获得经济效益参与的评价体系大力支持。对于现代文学概念说来,娱乐主题的重现意味了多方面的理论挑战：娱乐主题的位置,经典与“雅/俗”,大众的构成,商业、市场、传媒体系与文学。这些问题相互联系,持续发酵。现代文学概念应当开启多维的对话与争论,以主动的姿态回应现代社会制造的种种历史境遇。     

An all-organic aqueous potassium dual-ion battery

Benefiting from the environmental friendliness of organic electrodes and the high security of aqueous electrolyte,an all-organic aqueous potassium dual-ion full...