Applying Minimum-Risk Criterion to Stochastic Hub Location Problems

AbstractThis paper presents a new class of two-stage stochastic hub location (HL) programming problems with minimum-risk criterion, in which uncertain demands are characterized by random vector. Meanwhile we demonstrate that the twostage programming problem is equivalent to a single-stage stochastic P-model. Under mild assumptions, we develop a deterministic binary programming problem by using standardization, which is equivalent to a binary fractional programming problem. Moreover, we show that the relaxation problem of the binary fractional programming problem is a convex programming problem. Taking advantage of branch-and-bound method, we provide a number of experiments to illustrate the efficiency of the proposed modeling idea

Di-μ-chlorido-bis­{[4-chloro-2-(dimethyl­amino­meth­yl)phenyl-κ2 C 1,N]palladium(II)}

The title compound, [Pd2(C9H11ClN)2Cl2], consists of two Pd atoms which are bridged by two Cl atoms, forming a centrosymmetric binuclear complex with a square-planar coordination for each of the Pd atoms. The Pd atom is chelated by one N and one C atom from a 4-chloro-2-(dimethyl­amino­meth­yl)phenyl ligand, forming a five-membered ring (N—Pd—C—C—C). In the crystal structure, weak C—H ⋯Cl hydrogen bonds link the mol­ecules in rows

A Multiproduct Single-Period Inventory Management Problem under Variable Possibility Distributions

In multiproduct single-period inventory management problem (MSIMP), the optimal order quantity often depends on the distributions of uncertain parameters. However, the distribution information about uncertain parameters is usually partially available. To model this situation, a MSIMP is studied by credibilistic optimization method, where the uncertain demand and carbon emission are characterized by variable possibility distributions. First, the uncertain demand and carbon emission are characterized by generalized parametric interval-valued (PIV) fuzzy variables, and the analytical expressions about the mean values and second-order moments of selection variables are established. Taking second-order moment as a risk measure, a new credibilistic multiproduct single-period inventory management model is developed under mean-moment optimization criterion. Furthermore, the proposed model is converted to its equivalent deterministic model. Taking advantage of the structural characteristics of the deterministic model, a domain decomposition method is designed to find the optimal order quantities. Finally, a numerical example is provided to illustrate the efficiency of the proposed mean-moment credibilistic optimization method. The computational results demonstrate that a small perturbation of the possibility distribution can make the nominal optimal solution infeasible. In this case, the decision makers should employ the proposed credibilistic optimization method to find the optimal order quantities

Automated responsive web services evolution through generative aspect-oriented component adaptation

When building service oriented systems, it is often the case that existing web services do not perfectly match user requirements in target systems. To achieve smooth integration and high reusability of web services, mechanisms to support automated evolution of web services are highly in demand. This paper advocates achieving the above evolution by applying a highly automated aspect-oriented adaptation approach to the underlying components of web services by generating and then applying the adaptation aspects under designed weaving process according to specific adaptation requirements. An expandable library of reusable adaptation aspects at multiple abstraction levels has been developed. A prototype tool is developed to scale up the approach

Comparison of the clinical characteristics and prognosis between clear cell carcinomas and high-grade serous ovarian carcinomas

Objectives: To compare the clinical characteristics and prognosis of women with clear cell versus high-grade serous ovarian carcinoma. Material and methods: Retrospective analysis of the clinical data of 50 cases patients with ovarian clear cell carcinoma (OCCC) and 103 cases with high-grade serous ovarian carcinoma (HGSOC), who were initially treated and completed standardized therapy in Affiliated Hospital of Qingdao University from January 2013 to December 2017. Results: There were significant differences in age, gravidity (G > 1), chief complaint, with ovarian endometriosis, tumor diameter, unilateral or bilateral, cystic and solid tumor, CA125, HE4, CA199, lactate dehydrogenase (LDH), and FIGO stage between the two groups. The differences in the prognosis between OCCC patients and HGSOC patients with early stage (FIGO I–II) were not statistically significant. The 5-year overall survival and progression-free survival of OCCC patients were significantly worse than those of HGSOC patients with advanced stage (FIGO III–IV) (p < 0.05). FIGO stage and non-R0 resection were independent risk factors affecting the prognosis of patients with ovarian clear cell carcinoma, screening by Cox regression analysis. FIGO stage, the lowest value of CA125, and non-R0 resection were independent risk factors affecting the prognosis of patients with high-grade serous ovarian cancer. Conclusions: The clinical characteristics and prognosis of OCCC are different from those of HGSOC. Ovarian clear cell carcinoma (OCCC) patients have a significantly worse prognosis than those with HGSOC in the advanced stage (FIGO Ⅲ–Ⅳ). Satisfactory tumor resection is an essential factor related to the prognosis of patients with OCCC and HGSOC

Association of N-nitrosodimethylamine exposure with cognitive impairment based on the clues of mice and humans

N-nitrosodimethylamine (NDMA) is an environmental and food contaminant, but limited data to concern whether NDMA has adverse effects on the brain. This study first determined the concentration of NDMA in foods from aquaculture markets in Shenzhen, then analyzed the effects on C57BL/6 mice and further evaluated on the urine samples of elderly Chinese residents with normal cognition (NC, n = 144), cognitive decline (CD, n = 116) and mild cognitive impairment (MCI, n = 123). The excessive rate of NDMA in foods was 3.32% (27/813), with a exceeding range of 4.78–131.00 μg/kg. Behavioral tests showed that 60 days treatment of mice with 3 mg/kg NDMA reduced cognitive performance. Cognitive impairment in human was significantly associated with sex, educational levels, length of residence in Shenzhen, household registration, passive smoking, rice, fresh vegetables, bacon products. NDMA was detected in 55.4% (212/383) of urine samples, with a median concentration of 0.23 μg/L (1.20 × 10 –7–157.39 μg/L). The median concentration for NC, CD and MCI were 0.32, 0.27, and 0 μg/L, respectively. The urinary NDMA concentration had a strong negative correlation with cognitive impairment (Kendall’s Tau-b = −0.89, P = 0.024). The median estimated daily intake (EDI) of NDMA was determined to be 6.63 ng/kg-bw/day. Taken together, there appears to be an association between NDMA and human and murine cognition, which provides a new clue to Alzheimer’s disease (AD)

Equilibrium Mean Value of Random Fuzzy Variable and Its Convergence Properties

Abstract The equilibrium measure is a natural extension of both probability and credibility measures. The convergence modes of random fuzzy variables with respect to equilibrium measure is an important issue for research. In this paper, we first introduce several convergence concepts for sequences of random fuzzy variables, including convergence in equilibrium measure and convergence in equilibrium distribution. Then we deal with the properties of the convergence modes of random fuzzy variables. The equilibrium mean value of random fuzzy variable with respect to equilibrium measure is also defined by nonlinear integral. For sequence of integrable random fuzzy variables, we deal with the important monotone convergence theorems as well as dominated convergence theorems. The convergent results obtained in this paper have potential applications in the approximation scheme of equilibrium optimization models

MDAN-UNet: Multi-Scale and Dual Attention Enhanced Nested U-Net Architecture for Segmentation of Optical Coherence Tomography Images

Optical coherence tomography (OCT) is an optical high-resolution imaging technique for ophthalmic diagnosis. In this paper, we take advantages of multi-scale input, multi-scale side output and dual attention mechanism and present an enhanced nested U-Net architecture (MDAN-UNet), a new powerful fully convolutional network for automatic end-to-end segmentation of OCT images. We have evaluated two versions of MDAN-UNet (MDAN-UNet-16 and MDAN-UNet-32) on two publicly available benchmark datasets which are the Duke Diabetic Macular Edema (DME) dataset and the RETOUCH dataset, in comparison with other state-of-the-art segmentation methods. Our experiment demonstrates that MDAN-UNet-32 achieved the best performance, followed by MDAN-UNet-16 with smaller parameter, for multi-layer segmentation and multi-fluid segmentation respectively