Reliability-based Probabilistic Network Pricing with Demand Uncertainty

The future energy system embraces growing flexible demand and generation, which bring large-scale uncertainties and challenges to current deterministic network pricing methods. This paper proposes a novel reliability-based probabilistic network pricing method considering demand uncertainty. Network reliability performance, including probabilistic contingency power flow (PCPF) and tolerance loss of load (TLoL), are used to assess the impact of demand uncertainty on actual network investment cost, where PCPF is formulated by the combined cumulant and series expansion. The tail value at risk (TVaR) is used to generate analytical solutions to determine network reinforcement horizons. Then, final network charges are calculated based on the core of the Long-run incremental cost (LRIC) algorithm. A 15-bus system is employed to demonstrate the proposed method. Results indicate that the pricing signal is sensitive to both demand uncertainty and network reliability, incentivising demand to reduce uncertainties. This is the first-ever network pricing method that determines network investment costs considering both supply reliability and demand uncertainties. It can guide better sitting and sizing of future flexible demand in distribution systems to minimise investment costs and reduce network charges, thus enabling a more efficient system planning and cheaper integration.</p

Effects of duration of long-acting GnRH agonist downregulation on assisted reproductive technology outcomes in patients with adenomyosis: a retrospective cohort study

ObjectivesTo elucidate the relationship between long-acting GnRH agonist (GnRHa) downregulation and assisted reproductive technology (ART) outcomes and identify the optimal duration of downregulation in patients with adenomyosis.DesignRetrospective cohort study.ParticipantsThe study was designed to evaluate ART outcomes in adenomyosis patients with and without GnRHa downregulation between January 2016 and December 2020. A total of 374 patients with adenomyosis (621 cycles) were included with 281 cycles in downregulation group versus 340 cycles in non-downregulation group. After 1:1 propensity score matching (PSM), a sample size of 272 cycles in each group was matched. The matched downregulation group was further divided into 1-month (147 cycles), 2-months (72 cycles), and ≥3 months downregulation (53 cycles) subgroups. Stratification analysis was conducted on pregnancy outcomes in 239 fresh embryo transfer (ET) cycles and 305 frozen embryo transfer (FET) cycles.ResultsThe downregulation group had larger mean diameter of initial uterus and higher proportion of severer dysmenorrhea compared to non-downregulation group. The pregnancy-related parameters in GnRHa downregulation group were similar to those in non-downregulation group, except for higher late miscarriage rate (MR) (13.4% vs. 3.1%, P = 0.003). The subgroup comparisons in fresh ET cycles indicated that implantation rate (75.0% vs. 39.2%, P = 0.002), biochemical pregnancy rate (91.7% vs. 56.0%, P = 0.036) and clinical pregnancy rate (83.3% vs. 47.0%, P = 0.016) could be improved by prolonged GnRHa downregulation (≥3 months), whereas late MR was difficult to be reversed (30.0% vs. 3.2%, P = 0.017). In FET cycles, higher MR (53.6% vs. 29.9%, P = 0.029; 58.8% vs. 29.9%, P = 0.026) and lower live birth rate (18.8% vs. 34.1%, P = 0.023; 17.1% vs. 34.1%, P = 0.037) were observed in the 1-month and ≥3 months downregulation group, while no differences were found in the 2-months downregulation group compared to the non-downregulation group.ConclusionIn patients with severer adenomyosis, long-acting GnRHa downregulation might be correlated with improved ART outcomes. In fresh ET cycles, prolonged downregulation (≥3 months) might be beneficial to improve live birth rate, which needed to be verified by further study with larger sample. In FET cycles, the optimal duration of downregulation was not certain and still needed further exploration

Directory of English/Chinese Names of Scholars in Chinese Studies - 海外中国研究学者名录（英中对照）

The Directory of English/Chinese Names of Scholars in Chinese Studies was a by-product of the "Chinese Studies in North America - Research and Resources" project. It provides both the English and the Chinese names of scholars involved in Chinese Studies mainly in North America. The Chinese names for western scholars resulted from an extensive in the relevant literature and on the internet at appropriate sites to find and authenticate the Chinese names used by these scholars. Where we could not find the Chinese name adopted by a scholar, we have transliterated their name into Chinese characters using the standard reference book 英语姓名译名手册. It is hoped that this directory will be useful for people needing to search for the Chinese names used by western scholars, or for the standard transliterations of their names into Chinese characters. Corrections of inaccurate information and addition of new names of Chinese Studies scholars worldwide are welcome. For corrections, comments and updates, please send emails to Haihui Zhang (Librarian for Chinese studies at East Asian Library, University Library System at University of Pittsburgh) at [email protected]

Synthesis and fungicidal activity of pyrazole derivatives containing 1,2,3,4-tetrahydroquinoline

Additional file 3. Structural information (CIF) for Compound 10g

Myrica rubra Extracts Protect the Liver from CCl4-Induced Damage

The relationship between the expression of mitochondrial voltage-dependent anion channels (VDACs) and the protective effects of Myrica rubra Sieb. Et Zucc fruit extract (MCE) against carbon tetrachloride (CCl4)-induced liver damage was investigated. Pretreatment with 50 mg kg−1, 150 mg kg−1 or 450 mg kg−1 MCE significantly blocked the CCl4-induced increase in both serum aspartate aminotransferase (sAST) and serum alanine aminotransferase (sALT) levels in mice (P < .05 or .01 versus CCl4 group). Ultrastructural observations of decreased nuclear condensation, ameliorated mitochondrial fragmentation of the cristae and less lipid deposition by an electron microscope confirmed the hepatoprotection. The mitochondrial membrane potential dropped from −191.94 ± 8.84 mV to −132.06 ± 12.26 mV (P < .01) after the mice had been treated with CCl4. MCE attenuated CCl4-induced mitochondrial membrane potential dissipation in a dose-dependent manner. At a dose of 150 or 450 mg kg−1 of MCE, the mitochondrial membrane potentials were restored (P < .05). Pretreatment with MCE also prevented the elevation of intra-mitochondrial free calcium as observed in the liver of the CCl4-insulted mice (P < .01 versus CCl4 group). In addition, MCE treatment (50–450 mg kg−1) significantly increased both transcription and translation of VDAC inhibited by CCl4. The above data suggest that MCE mitigates the damage to liver mitochondria induced by CCl4, possibly through the regulation of mitochondrial VDAC, one of the most important proteins in the mitochondrial outer membrane

Optimal day-ahead scheduling for active distribution network based on improved information gap decision theory

In this study, information gap decision theory (IGDT) is reformed to formulate the uncertain parameters of wind power, photovoltaic and load. Traditional IGDT presumes that positive and negative deviations of uncertain parameters of the predicted value are equal, and it would result in imprecise assessment of fluctuated intervals. This study proposes an improved IGDT to overcome the inaccuracy of traditional IGDT by considering unsymmetrical fluctuation levels of uncertainties. For the operation and control of active distribution network, the non-linear power flow constraints are included and linearised with a novel method based on circumscribed polyhedron approximation, which guarantees the accuracy of the solution results and takes less computing time. Additionally, from the mathematical point of view, the model established in this study is a multilevel optimisation problem, and linear Karush–Kuhn–Tucker conditions are formulated to transform the multilevel optimisation problem into a single-level optimisation problem. Finally, the economic viability and model applicability are verified through the modified IEEE 33-node distribution system

Multicomponent Therapeutics of Berberine Alkaloids

Although berberine alkaloids (BAs) are reported to be with broad-spectrum antibacterial and antiviral activities, the interactions among BAs have not been elucidated. In the present study, methicillin-resistant Staphylococcus aureus (MRSA) was chosen as a model organism, and modified broth microdilution was applied for the determination of the fluorescence absorption values to calculate the anti-MRSA activity of BAs. We have initiated four steps to seek the optimal combination of BAs that are (1) determining the anti-MRSA activity of single BA, (2) investigating the two-component combination to clarify the interactions among BAs by checkerboard assay, (3) investigating the multicomponent combination to determine the optimal ratio by quadratic rotation-orthogonal combination design, and (4) in vivo and in vitro validation of the optimal combination. The results showed that the interactions among BAs are related to their concentrations. The synergetic combinations included “berberine and epiberberine,” “jatrorrhizine and palmatine” and “jatrorrhizine and coptisine”; the antagonistic combinations included “coptisine and epiberberine”. The optimal combination was berberine : coptisine : jatrorrhizine : palmatine : epiberberine = 0.702 : 0.863 : 1 : 0.491 : 0.526, and the potency of the optimal combination on cyclophosphamide-immunocompromised mouse model was better than the natural combinations of herbs containing BAs

Disruption of Nrf2 Enhances Upregulation of Nuclear Factor-κB Activity, Proinflammatory Cytokines, and Intercellular Adhesion Molecule-1 in the Brain after Traumatic Brain Injury

Inflammatory response plays an important role in the pathogenesis of secondary brain injury after traumatic brain injury (TBI). Nuclear factor erythroid 2-related factor 2 (Nrf2) is a key transcription factor that plays a crucial role in cytoprotection against inflammation. The present study investigated the role of Nrf2 in the cerebral upregulation of NF-κB activity, proinflammatory cytokine, and ICAM-1 after TBI. Wild-type Nrf2 (+/+) and Nrf2 (−/−)-deficient mice were subjected to a moderately severe weight-drop impact head injury. Electrophoretic mobility shift assays (EMSAs) were performed to analyze the activation of nuclear factor kappa B (NF-κB). Enzyme-linked immunosorbent assays were performed to quantify the production of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6). Immunohistochemistry staining experiments were performed to detect the expression of intercellular adhesion molecule-1 (ICAM-1). Nrf2 (−/−) mice were shown to have more NF-κB activation, inflammatory cytokines TNF-α, IL-1β and IL-6 production, and ICAM-1 expression in brain after TBI compared with their wild-type Nrf2 (+/+) counterparts. The results suggest that Nrf2 plays an important protective role in limiting the cerebral upregulation of NF-κB activity, proinflammatory cytokine, and ICAM-1 after TBI

VITAL: VIsual Tracking via Adversarial Learning

The tracking-by-detection framework consists of two stages, i.e., drawing samples around the target object in the first stage and classifying each sample as the target object or as background in the second stage. The performance of existing trackers using deep classification networks is limited by two aspects. First, the positive samples in each frame are highly spatially overlapped, and they fail to capture rich appearance variations. Second, there exists extreme class imbalance between positive and negative samples. This paper presents the VITAL algorithm to address these two problems via adversarial learning. To augment positive samples, we use a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes. With the use of adversarial learning, our network identifies the mask that maintains the most robust features of the target objects over a long temporal span. In addition, to handle the issue of class imbalance, we propose a high-order cost sensitive loss to decrease the effect of easy negative samples to facilitate training the classification network. Extensive experiments on benchmark datasets demonstrate that the proposed tracker performs favorably against state-of-the-art approaches.Comment: Spotlight in CVPR 201