Shandong University

About: Shandong University is a education organization based out in Jinan, Shandong, China. It is known for research contribution in the topics: Laser & Cancer. The organization has 99070 authors who have published 99160 publications receiving 1625094 citations. The organization is also known as: Shāndōng Dàxué.

A review of the open charm and open bottom systems

Abstract: Since the discovery of the first charmed meson in 1976, many open-charm and open-bottom hadrons were observed. In 2003 two narrow charm-strange states $D_{s0}^*(2317)$ and $D_{s1}(2460)$ were discovered by the BaBar and CLEO Collaborations, respectively. After that, more excited heavy hadrons were reported. In this work, we review the experimental and theoretical progress in this field.

A General Stochastic Maximum Principle for SDEs of Mean-field Type

Abstract: We study the optimal control for stochastic differential equations (SDEs) of mean-field type, in which the coefficients depend on the state of the solution process as well as of its expected value. Moreover, the cost functional is also of mean-field type. This makes the control problem time inconsistent in the sense that the Bellman optimality principle does not hold. For a general action space a Peng’s-type stochastic maximum principle (Peng, S.: SIAM J. Control Optim. 2(4), 966–979, 1990) is derived, specifying the necessary conditions for optimality. This maximum principle differs from the classical one in the sense that here the first order adjoint equation turns out to be a linear mean-field backward SDE, while the second order adjoint equation remains the same as in Peng’s stochastic maximum principle.

Enhanced degradation of clothianidin in peroxymonosulfate/catalyst system via core-shell FeMn @ N-C and phosphate surrounding

Abstract: FeMn bimetallic nanoparticles coated with nitrogen-doped graphene shells (FeMn@N-C) were prepared to decompose clothianidin (CTD) through activation of peroxymonosulfate (PMS). The redox cycles of encapsulated FeMn bimetallic nanoparticles with multiple valence states could accelerate the radical generation and ensure the stability of FeMn@N-C with continuous PMS activation. Coexisting NaH2PO4 could unusually accelerate CTD degradation through facilitating reactive oxygen species (ROS) generation and decreasing bonding dissociation energies of CTD molecule. DFT calculation indicated that the C-Cl bond in CTD molecule was weakened after being surrounded by NaH2PO4. Two main degradation pathways were proposed in the absence of phosphate and a new dechlorination pathway was first reported after CTD molecule bonding with phosphate, which was confirmed by DFT. This work provides a feasible strategy of PMS activation by graphene coated bimetallic nanoparticles and give a new insight into accelerated degradation of recalcitrant pollutants with phosphate surrounding.

Prevalence of cardiovascular disease risk factor in the Chinese population: the 2007-2008 China National Diabetes and Metabolic Disorders Study.

Abstract: Aims Cardiovascular disease (CVD) is now the most prevalent and debilitating disease affecting the Chinese population. The goal of the present manuscript was to analyse cardiovascular risk factors and the prevalence of non-fatal CVDs from data gathered from the 2007–2008 China National Diabetes and Metabolic Disorders Study. Methods and results A nationally representative sample of 46 239 adults, 20 years of age or older, was randomly recruited using a multistage stratified design method. Lifestyle factors, diagnosis of CVD, stroke, diabetes, and family history of each subject were collected, and an oral glucose tolerance test or a standard meal test was performed. Various non-fatal CVDs were reported by the subjects. SUDAAN software was used to perform all weighted statistical analyses, with P < 0.05 considered statistically significant. The prevalence of coronary heart disease, stroke, and CVDs was 0.74, 1.07, and 1.78% in males; and 0.51, 0.60, and 1.10% in females, respectively. The presence of CVDs increased with age in both males and females. The prevalence of being overweight or obese, hypertension, dyslipidaemia, or hyperglycaemia was 36.67, 30.09, 67.43, and 26.69% in males; and 29.77, 24.79, 63.98, and 23.62% in females, respectively. In the total sample of 46 239 patients, the prevalence of one subject having 1, 2, 3, or ≥4 of the 5 defined risk factors (i.e. smoking, overweight or obese, hypertension, dyslipidaemia, or hyperglycaemia) was 31.17, 27.38, 17.76, and 10.19%, respectively. Following adjustment for gender and age, the odds ratio of CVDs for those who had 1, 2, 3, or ≥4 risk factors was 2.36, 4.24, 4.88, and 7.22, respectively, when compared with patients with no risk factors. Conclusion Morbidity attributed to the five defined cardiovascular risk factors was high in the Chinese population, with multiple risk factors present in the same individual. Therefore, reasonable prevention strategies should be designed to attenuate the rapid rise in cardiovascular morbidity.

An Adaptive Semisupervised Feature Analysis for Video Semantic Recognition

Abstract: Video semantic recognition usually suffers from the curse of dimensionality and the absence of enough high-quality labeled instances, thus semisupervised feature selection gains increasing attentions for its efficiency and comprehensibility. Most of the previous methods assume that videos with close distance (neighbors) have similar labels and characterize the intrinsic local structure through a predetermined graph of both labeled and unlabeled data. However, besides the parameter tuning problem underlying the construction of the graph, the affinity measurement in the original feature space usually suffers from the curse of dimensionality. Additionally, the predetermined graph separates itself from the procedure of feature selection, which might lead to downgraded performance for video semantic recognition. In this paper, we exploit a novel semisupervised feature selection method from a new perspective. The primary assumption underlying our model is that the instances with similar labels should have a larger probability of being neighbors. Instead of using a predetermined similarity graph, we incorporate the exploration of the local structure into the procedure of joint feature selection so as to learn the optimal graph simultaneously. Moreover, an adaptive loss function is exploited to measure the label fitness, which significantly enhances model’s robustness to videos with a small or substantial loss. We propose an efficient alternating optimization algorithm to solve the proposed challenging problem, together with analyses on its convergence and computational complexity in theory. Finally, extensive experimental results on benchmark datasets illustrate the effectiveness and superiority of the proposed approach on video semantic recognition related tasks.