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Voltage source converter based high voltage direct current transmission (VSC-HVDC) is widely adopted in the power system with new energy sources, but with the problem of fast rise of fault current. Magnetic saturation fault current limiter (MSFCL) suppresses the rise of short-circuit current by increasing the inductance value, which has the advantages of high speed and no additional control. However, the change of inductance will affect the detection of VSC-HVDC system fault, may leading to missed or incorrect removal of the fault and causing harm. Based on this problem, this paper proposes a fault detection method used in VSC-HVDC system considering MSFCL, which has fast detection speed, high sensitivity and reliability. The proposed method is robust to short-circuit resistance variation and can locate faults external and internal. Firstly, we establish the short-circuit equivalent circuit of VSC-HVDC system. Then we establish the differential equation and solve it, to obtain the analytical expression of voltage across MSFCL. Furthermore, we proposed a short circuit fault detection method based on the rise rate of the voltage across MSFCL. Finally we establish a four port VSC-HVDC system simulation model to prove that the method is effective. The results show that compared with traditional methods, the proposed method has faster detection speed. The proposed method is independent of the power flow direction, can identify the short circuit fault with large short-circuit resistance of 500 Ω and can discriminate the faulty branch from the healthy ones accurately.

Abstract Depressive disorder prevalence in patients with schizophrenia has been reported to be 40%. People with low socioeconomic status (SES) are more likely to suffer from schizophrenia and major depressive disorder (MDD). However, the causal relationship between schizophrenia and depression and the potential mediating role of SES remains unclear. Two-sample Mendelian randomization (MR) analyses were conducted to explore the bidirectional causal relationship between schizophrenia and MDD with the largest sample size of European ancestry from public genome-wide association studies (sample size ranged from 130,644 to 480,359). Inverse variance weighted (IVW) method was used as the primary analysis, and several canonical MR methods were used as validation analyses. The mediating role of SES (educational years, household income, employment status, and Townsend deprivation index) was estimated by the two-step MR method. MR analyses showed that genetically predicted schizophrenia was associated with an increased risk of MDD (IVW odds ratio [OR] = 1.137 [95% CI 1.095, 1.181]). Reversely, MDD was also associated with an increased risk of schizophrenia (IVW OR = 1.323 [95% CI 1.118, 1.565]). The mediation analysis via the two-step MR method revealed that the causal effect of schizophrenia on MDD was partly mediated by the Townsend deprivation index with a proportion of 10.27%, but no significant mediation effect was found of SES on the causal effect of MDD on schizophrenia. These results suggest a robust bidirectional causal effect between schizophrenia and MDD. Patients with schizophrenia could benefit from the early and effective intervention of the Townsend deprivation index.

Abstract Constructing photocatalytically active and stable covalent organic frameworks containing both oxidative and reductive reaction centers remain a challenge. In this study, benzotrithiophene-based covalent organic frameworks with spatially separated redox centers are rationally designed for the photocatalytic production of hydrogen peroxide from water and oxygen without sacrificial agents. The triazine-containing framework demonstrates high selectivity for H2O2 photogeneration, with a yield rate of 2111 μM h−1 (21.11 μmol h−1 and 1407 μmol g−1 h−1) and a solar-to-chemical conversion efficiency of 0.296%. Codirectional charge transfer and large energetic differences between linkages and linkers are verified in the double donor-acceptor structures of periodic frameworks. The active sites are mainly concentrated on the electron-acceptor fragments near the imine bond, which regulate the electron distribution of adjacent carbon atoms to optimally reduce the Gibbs free energy of O2* and OOH* intermediates during the formation of H2O2.

Abstract Organic small-molecule contrast agents have attracted considerable attention in the field of multispectral optoacoustic imaging, but their weak optoacoustic performance resulted from relatively low extinction coefficient and poor water solubility restrains their widespread applications. Herein, we address these limitations by constructing supramolecular assemblies based on cucurbit[8]uril (CB[8]). Two dixanthene-based chromophores (DXP and DXBTZ) are synthesized as the model guest compounds, and then included in CB[8] to prepare host-guest complexes. The obtained DXP-CB[8] and DXBTZ-CB[8] display red-shifted and increased absorption as well as decreased fluorescence, thereby leading to a substantial enhancement in optoacoustic performance. Biological application potential of DXBTZ-CB[8] is investigated after co-assembly with chondroitin sulfate A (CSA). Benefiting from the excellent optoacoustic property of DXBTZ-CB[8] and the CD44-targeting feature of CSA, the formulated DXBTZ-CB[8]/CSA can effectively detect and diagnose subcutaneous tumors, orthotopic bladder tumors, lymphatic metastasis of tumors and ischemia/reperfusion-induced acute kidney injury in mouse models with multispectral optoacoustic imaging.

Summary: Short-term memory (STM) maintains information during a short delay period. How long-range and local connections interact to support STM encoding remains elusive. Here, we tackle the problem focusing on long-range projections from the medial prefrontal cortex (mPFC) to the anterior agranular insular cortex (aAIC) in head-fixed mice performing an olfactory delayed-response task. Optogenetic and electrophysiological experiments reveal the behavioral importance of the two regions in encoding STM information. Spike-correlogram analysis reveals strong local and cross-region functional coupling (FC) between memory neurons encoding the same information. Optogenetic suppression of mPFC-aAIC projections during the delay period reduces behavioral performance, the proportion of memory neurons, and memory-specific FC within the aAIC, whereas optogenetic excitation enhances all of them. mPFC-aAIC projections also bidirectionally modulate the efficacy of STM-information transfer, measured by the contribution of FC spiking pairs to the memory-coding ability of following neurons. Thus, prefrontal projections modulate insular neurons’ functional connectivity and memory-coding ability to support STM.

Study region: The Xiying River Basin is a typical inland mountainous watershed located in the Qilian Mountains. Study focus: This article reveals the change of runoff stable isotopes and their control mechanisms based on isotope data of different water bodies (precipitation, runoff, groundwater, and glacial snowmelt) in the Xiying River Basin from 2016 to 2021, combined with relevant meteorological observation data and the Normalized Vegetation Index (NDVI). New hydrological insights for the region: The results show that (1) meteorological factors control runoff isotope variations through controlling the evaporation process of runoff and the isotopic characteristics of recharge sources. (2) In areas with low vegetation cover, surface landscape features influence runoff isotopic composition by altering the isotopic characteristics of recharge sources. (3) In areas with high vegetation cover, surface vegetation cover affects runoff stable isotopes by controlling evapotranspiration processes. This study connects vegetation cover and runoff isotopes to explain the surface landscape control of runoff stable isotopes. It offers a more powerful reference for elucidating the factors influencing runoff isotopes in inland mountain watersheds.

Background: Stroke risk factors may contribute to cognitive decline and dementia by altering brain tissue integrity. If their effects on brain are nonnegligible, the target regions for stroke rehabilitation with brain stimulation identified by cross-sectional case-control studies may be biased due to the pre-existing brain differences caused by these risk factors. Here, we investigated the effects of stroke risk factors on cortical thickness (CT) and surface area (SA) in individuals without a history of stroke. Methods: In this observational study, we used data from the UK Biobank cohort to explore the effects of polygenic risk score for ischemic stroke (PRSIS), systolic blood pressure (SBP), diastolic blood pressure (DBP), glycated hemoglobin (HbA1c), triglycerides (TG), and low-density lipoprotein (LDL) on CT and SA of 62 cerebral regions. We excluded non-Caucasian participants and participants with missing data, unqualified brain images, or a history of stroke or any other brain diseases. We constructed a multivariate linear regression model for each phenotype to simultaneously test the effect of each factor and interaction between factors. The results were verified by sensitivity analyses of SDP or DBP input and adjusting for body-mass index, high-density lipoprotein cholesterol, or smoking and alcohol intake. By excluding participants with abnormal blood pressure, glucose, or lipid, we tested whether vascular risk factor within normal range also affected cortical phenotypes. To determine clinical relevance of our findings, we also investigated the effects of stroke risk factors and cortical phenotypes on cognitive decline assessed by fluid intelligence score (FIQ) and the mediation of cortical phenotype for the association between stroke risk factor and FIQ. Results: The study consisted of 27 120 eligible participants. Stroke risk factors were associated with 16 CT and two SA phenotypes in both main and sensitivity analyses (all p

Abstract Lightning often causes death, injury, and damage to various facilities and equipment. Accurately detecting the spatial location of lightning occurrence by predicting thunderstorms and lightning is of great significance. Traditional lightning detection systems detect lightning by measuring the sound, light, and electromagnetic field information radiated by lightning. These methods typically have two problems. First, the detection process of lightning signals is susceptible to electromagnetic interference. Second, the equipment cost is high and is not friendly to some lightning detection tasks only targeted at specific scenarios. In order to detect lightning more conveniently, we propose a lightning detection model based on deep learning networks. With the increase in the use of cameras in modern society, designing lightning object detection networks based on deep learning is possible. However, two problems have been found in existing practice: (1) When strong lightning meteorological phenomena occur, the lightning features in the image are covered by bright electric lights, and convolutional neural networks cannot distinguish between strong lightning scenes and strong ultraviolet scenes. (2) The performance of convolutional neural networks is often related to the model's size. The larger the model, the stronger the performance of the network. However, in practical application scenarios, computing resources are insufficient to use sufficiently large networks. In this paper, we propose a simple and effective lightning object detection network (LD‐Net) and use a foreground‐background segmentation algorithm to locate frames containing lightning in the video. After using the knowledge distillation‐based model compression method, the mAP of the lightning object detection network with a backbone net of resnet with 18‐layer (LD‐Net‐18) can reach 82.4%. We hope that the proposed LD‐Net can serve as a simple and powerful alternative to traditional lightning detection methods, enhancing efficiency in lightning detection tasks.