Robust approximation of chance constrained optimization with polynomial perturbation

This paper studies a robust approximation method for solving a class of chance constrained optimization problems. The constraints are assumed to be polynomial in the random vector. Under the assumption, the robust approximation of the chance constrained optimization problem can be reformulated as an optimization problem with nonnegative polynomial conic constraints. A semidefinite relaxation algorithm is proposed for solving the approximation. Its asymptotic and finite convergence are proven under some mild assumptions. In addition, we give a framework for constructing good uncertainty sets in the robust approximation. Numerical experiments are given to show the efficiency of our approach.Comment: 25 page

Blood Urea Nitrogen as a Predictor of Severe Acute Pancreatitis Based on the Revised Atlanta Criteria: Timing of Measurement and Cutoff Points

Background and Aims. This study evaluated the prognostic accuracy of BUN for severe acute pancreatitis (SAP) and in-hospital mortality (IHM) in terms of the best timing for BUN measurement and the optimal BUN cutoff points. Methods. BUN determinants at the time of admission and 24 hrs after hospital admission were recorded and analyzed statistically. The ability of BUN in predicting the SAP and the occurrence of IHM were assessed using the area under the receiver-operating characteristic (ROC) curve. Results. For SAP, AUC of BUN at admission and 24 hrs after hospital admission was 0.75 and 0.80, respectively. For IHM in acute pancreatitis, it was 0.86 at admission and 0.84 after 24 hrs of hospital admission, respectively. The optimal cutoff point of BUN 24 hrs after hospital admission for SAP and at admission for IHM was 8.3 mmol/L and 13.3 mmol/L, respectively. Conclusion. BUN determination after 24 hrs of hospital admission has high accuracy for prediction of SAP while BUN at initial admission has high accuracy for prediction of IHM

Dynamic security control in heat and electricity integrated energy system with an equivalent heating network model

Intensified interaction between electric power system (EPS) and district heating network (DHN) has raised an urgent requirement of security control to ensure the regular operation of the heat and electricity integrated energy system (HE-IES). However, the implicit expressions of thermal dynamics remain an obstacle to building the quantitative relationships between the EPS and the DHN. To solve this, this paper proposes a novel dynamic model of the district heating network, which contains the equivalent pipe model and the analytical thermal source-load function (TSLF). Then, the interaction between EPS and DHN is divided into the energy flow calculation at the control points and the dynamic simulation during the control intervals to investigate the coupling effect. On this basis, the dynamic characteristics in HE-IES are employed to quantitatively establish the security control strategy using the combined sensitivity analysis. Case studies verify the accuracy and efficiency of the proposed method

Partitional decoupling method for fast calculation of energy flow in a large-scale heat and electricity integrated energy system

Wide development of the heat and electricity integrated energy system (HE-IES) has provided flexible multi-energy optimization management, which improves energy efficiency and benefits the environment. It is essential to calculate energy flow quickly, accurately, and reliably as the system scale and complexity increase. To handle this problem, we propose a partitional decoupling method based on a decomposed energy flow model for the large-scale HE-IES. The computation mechanisms are presented considering various system scales and topologies, which consist of decoupling mode to solve the non-convergence caused by hydraulic initialization and the iteration mode to correct the variables at common nodes of decoupled systems. Moreover, a unit conversion method is introduced to solve the non-convergence problem in energy flow calculation under extreme scenarios. Case studies show that the proposed method provides accurate energy flow calculations with higher efficiency regardless of system scale and topology, especially in looped systems. Numerical simulation further verifies the effectiveness of the proposed method for non-convergence

Dynamic energy flow analysis of the heat-electricity integrated energy systems with a novel decomposition-iteration algorithm

Simulation and operation optimization studies on the integrated energy system have received extensive attention recently for its potential in improving energy efficiency and increasing grid integration of renewable energy, where the task of energy flow calculation serves as a fundamental tool to determine the network states. This paper investigates the models and methods for dynamic energy flow analysis of two strongly coupled networks in the integrated energy systems — the power grid and the heating network. First, the complicated coupling mechanisms of power grid and heating network are thoroughly analyzed and classified into four representative coupling modes. On this basis, the detailed dynamic energy flow analysis method for each coupling mode is developed. Second, a refined difference scheme is applied to discretize the partial differential equations describing the long-lasting temperature dynamics in the heating network. The high-dimensional dicretized model is then solved by a novel decomposition-iteration algorithm. Compared with existing methods, this algorithm avoids deriving the gigantic coefficient matrix of network equations and can improve the accuracy of energy flow results. Finally, considering the systematical error caused by neglecting the inertial and adjusting constraints of heat sources, a revision stage is firstly introduced to correct the heat power output of the slack source and help obtain more accurate energy flow results. Case study shows that the proposed methods take 3.21 s to obtain the dynamic energy flows of a coupled system consisting of a 118-node power grid and eight 35-node district heating networks over a 300-minutes simulation course, which is qualified to provide support for simulation and optimization related applications in practice

High-Density Lipoprotein Cholesterol, Blood Urea Nitrogen, and Serum Creatinine Can Predict Severe Acute Pancreatitis

Background and Aims. Early prediction of disease severity of acute pancreatitis (AP) would be helpful for triaging patients to the appropriate level of care and intervention. The aim of the study was to develop a model able to predict Severe Acute Pancreatitis (SAP). Methods. A total of 647 patients with AP were enrolled. The demographic data, hematocrit, High-Density Lipoprotein Cholesterol (HDL-C) determinant at time of admission, Blood Urea Nitrogen (BUN), and serum creatinine (Scr) determinant at time of admission and 24 hrs after hospitalization were collected and analyzed statistically. Results. Multivariate logistic regression indicated that HDL-C at admission and BUN and Scr at 24 hours (hrs) were independently associated with SAP. A logistic regression function (LR model) was developed to predict SAP as follows: −2.25–0.06 HDL-C (mg/dl) at admission + 0.06 BUN (mg/dl) at 24 hours + 0.66 Scr (mg/dl) at 24 hours. The optimism-corrected c-index for LR model was 0.832 after bootstrap validation. The area under the receiver operating characteristic curve for LR model for the prediction of SAP was 0.84. Conclusions. The LR model consists of HDL-C at admission and BUN and Scr at 24 hours, representing an additional tool to stratify patients at risk of SAP

An insecticide target in mechanoreceptor neurons

Hundreds of neurotoxic insecticides are currently in use. However, only a few direct targets have been identified. Here, using Drosophila and the insecticide flonicamid, we identified nicotinamidase (Naam) as a previous unidentified molecular target for an insecticide. Naam is expressed in chordotonal stretch-receptor neurons, and inhibition of Naam by a metabolite of flonicamid, TFNA-AM (4-trifluoromethylnicotinamide), induces accumulation of substrate nicotinamide and greatly inhibits negative geotaxis. Engineered flies harboring a point mutation in the active site show insecticide resistance and defects in gravity sensing. Bees are resistant to flonicamid because of a gene duplication, resulting in the generation of a TFNA-AM-insensitive Naam. Our results, in combination with the absence of genes encoding Naam in vertebrate genomes, suggest that TFNA-AM and potential species-specific Naam inhibitors could be developed as novel insecticides, anthelmintics, and antimicrobials for agriculture and human health

De Novo Assembly, Characterization and Comparative Transcriptome Analysis of the Mature Gonads in <i>Spinibarbus hollandi</i>

Spinibarbus hollandi is an important commercial aquaculture species in southeastern China, but with long maturity period and low egg laying amount. However, there has been little study of its gonad development and reproductive regulation, which limits aquaculture production. Here, for the first time, gonadal transcriptomes of male and female S. hollandi were analyzed. A total of 167,152 unigenes were assembled, with only 48,275 annotated successfully. After comparison, a total of 21,903 differentially expressed genes were identified between male and female gonads, of which 16,395 were upregulated and 5508 were downregulated in the testis. In addition, a large number of differentially expressed genes participating in reproduction, gonad formation and differentiation, and gametogenesis were screened out and the differential expression profiles of partial genes were further validated using quantitative real-time PCR. These results will provide basic information for further research on gonad differentiation and development in S. hollandi

Magnesium Isoglycyrrhizinate Reduces the Target-Binding Amount of Cisplatin to Mitochondrial DNA and Renal Injury through SIRT3

Nephrotoxicity is the dose-limiting factor of cisplatin treatment. Magnesium isoglycyrrhizinate (MgIG) has been reported to ameliorate renal ischemia&ndash;reperfusion injury. This study aimed to investigate the protective effect and possible mechanisms of MgIG against cisplatin-induced nephrotoxicity from the perspective of cellular pharmacokinetics. We found that cisplatin predominantly accumulated in mitochondria of renal tubular epithelial cells, and the amount of binding with mitochondrial DNA (mtDNA) was more than twice that with nuclear DNA (nDNA). MgIG significantly lowered the accumulation of cisplatin in mitochondria and, in particular, the degree of target-binding to mtDNA. MgIG notably ameliorated cisplatin-induced changes in mitochondrial membrane potential, morphology, function, and cell viability, while the magnesium donor drugs failed to work. In a mouse model, MgIG significantly alleviated cisplatin-caused renal dysfunction, pathological changes of renal tubules, mitochondrial ultrastructure variations, and disturbed energy metabolism. Both in vitro and in vivo data showed that MgIG recovered the reduction of NAD+-related substances and NAD+-dependent deacetylase sirtuin-3 (SIRT3) level caused by cisplatin. Furthermore, SIRT3 knockdown weakened the protective effect of MgIG on mitochondria, while SIRT3 agonist protected HK-2 cells from cisplatin and specifically reduced platinum-binding activity with mtDNA. In conclusion, MgIG reduces the target-binding amount of platinum to mtDNA and exerts a protective effect on cisplatin-induced renal injury through SIRT3, which may provide a new strategy for the treatment of cisplatin-induced nephrotoxicity

Serum Albumin Is Independently Associated with Persistent Organ Failure in Acute Pancreatitis

Background and Aims. To investigate the association between serum albumin levels within 24 hrs of patient admission and the development of persistent organ failure in acute pancreatitis. Methods. A total of 700 patients with acute pancreatitis were enrolled. Multivariate logistic regression and subgroup analysis determined whether decreased albumin was independently associated with persistent organ failure and mortality. The diagnostic performance of serum albumin was evaluated by the area under Receiver Operating Characteristic (ROC) curves. Results. As levels of serum albumin decrease, the risk of persistent organ failure significantly increases (Ptrend<0.001). The incidence of organ failure was 3.5%, 10.6%, and 41.6% in patients with normal albumin and mild and severe hypoalbuminaemia, respectively. Decreased albumin levels were also proportionally associated with prolonged hospital stay (Ptrend<0.001) and the risk of death (Ptrend<0.001). Multivariate analysis suggested that biliary etiology, chronic concomitant diseases, hematocrit, blood urea nitrogen, and the serum albumin level were independently associated with persistent organ failure. Blood urea nitrogen and the serum albumin level were also independently associated with mortality. The area under ROC curves of albumin for predicting organ failure and mortality were 0.78 and 0.87, respectively. Conclusion. A low serum albumin is independently associated with an increased risk of developing of persistent organ failure and death in acute pancreatitis. It may also be useful for the prediction of the severity of acute pancreatitis