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39 results on '"Meng, Ling-yan"'

12. The Effect of a 20-Hour Baby-Friendly Hospital Initiative Training Program on Nurses' Breastfeeding Knowledge, Attitudes and Confidence, in a Tertiary Hospital in Singapore.

Author

Fok, Doris, Chang, Hsin Fang, Meng, Ling Yan, and Ng, Yvonne Peng Mei

Subjects
NURSING, NURSES' attitudes, CONFIDENCE, CLINICAL trials, ACADEMIC medical centers, NEONATAL intensive care, EVALUATION of human services programs, BREASTFEEDING promotion, HEALTH facility administration, BREAST milk, TERTIARY care, NEONATAL intensive care units, SELF medication, SURVEYS, PRE-tests & post-tests, HUMAN services programs, COMPARATIVE studies, BREASTFEEDING, HOSPITAL nursing staff, NURSES, PEDIATRIC nursing, MILK substitutes
Abstract

Objective  The Baby-Friendly Hospital Initiative (BFHI) enables maternity units to be centers of breastfeeding support to increase breastfeeding rates. This study evaluates the impact of the 20-hour BFHI training course on nurses' breastfeeding knowledge, attitude, and confidence in breastfeeding practice in a tertiary hospital in Singapore. Study Design  Seventeen sessions of the 20-hour BFHI training course were conducted by lactation consultants from 2010 to 2013 at the National University Hospital, Singapore. An anonymous self-administered survey on knowledge, attitude, and confidence in breastfeeding practices were distributed to nurses before (2009) and after (2014) the training courses to assess effectiveness of training. Results  One-hundred forty nurses and one hundred forty-eight nurses participated in the surveys in 2009 and 2014, respectively. Majority were registered nurses who worked in the postnatal wards and the neonatal intensive care unit. After training, there were significant improvements for five of eight items in infant feeding knowledge, including greater awareness of the International Code of Marketing of Breastmilk Substitutes and medical contraindication for breastfeeding. Participants reported more confidence in assisting mothers on breastfeeding, 77.1 to 88.5% (p  = 0.019); advising hand expressing breast milk, 75.7 to 86.5% (p  = 0.012); and advising attachment to the breast, 75.7 to 89.2% (p  = 0.004) in 2014 compared with 2009. However, despite having high levels of confidence, only about half the nurses reported being able to assist mothers in breastfeeding, mainly due to time constraints. Conclusion  Implementation of the 20-hour BFHI training program positively influenced nurses' breastfeeding knowledge, attitude, and confidence in breastfeeding practices. Hospital procedures and manpower requirements should be re-examined to overcome nursing constraints in providing breastfeeding help to postpartum mothers. Key Points Nurses have low breastfeeding knowledge pretraining. The 20-hour BFHI training course is effective. Nurses have inadequate time to support breastfeeding. [ABSTRACT FROM AUTHOR]

Published
2022
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14. Cu/C or Cu2O/C Composites: Selective Synthesis, Characterization, and Applications in Water Treatment

Author

Liu Yan-Jun, FU Lian-Hua, Liu Shan, Meng Ling-Yan, Dong Yan-Yan, Li Ya-Yu, and Ma Ming-Guo

Subjects
Materials science, Chemical engineering, General Materials Science, Water treatment, 02 engineering and technology, 010501 environmental sciences, 021001 nanoscience & nanotechnology, 0210 nano-technology, 01 natural sciences, 0105 earth and related environmental sciences, Characterization (materials science)
Published
2016

19. A novel fitness allocation algorithm for maintaining a constant selective pressure during GA procedure

Author

Yuan Xu, Chenxi Zhou, Rong He, Li Feng Zhang, and Meng Ling Yan

Subjects
education.field_of_study, Mathematical optimization, Fitness function, Fitness approximation, Cognitive Neuroscience, Crossover, Population, Evolutionary algorithm, Computer Science Applications, Artificial Intelligence, Fitness proportionate selection, Genetic algorithm, education, Selection (genetic algorithm), Mathematics
Abstract

During the evolution procedure of GA, the fitness distribution of population is always unforeseeable since it varies with many factors such as the nonlinearity and multimodality of optimization problem, crossover and mutation algorithms, and the progress of evolution procedure. For the GAs using stochastic selection mechanisms, fitness distribution sometimes significantly impacts on the performance of selection process, so as to make it very ineffective in protecting superior individuals and preventing inferior individuals. In this study, a new fitness scaling method, named powered distance sums scaling (PDSS), is proposed to eliminate the influence of fitness distribution on stochastic selection. Unlike previous approaches, the new method uses the powered sums of fitness distance to substitute for both raw fitness values and ranking numbers for computing scaled fitness. It maintains a much more constant and consistent selective pressure in different conditions of optimization problem and GA algorithm design, and may help GA designers in balancing exploration and exploitation during evolution procedures. Empirical studies are employed to illustrate the new method that through using the new scaling technique the convergence speed of GA search becomes more controllable.

Published
2015

27. “Smart” Ag Nanostructures for Plasmon-Enhanced Spectroscopies

Author

Li, Chao-Yu, primary, Meng, Meng, additional, Huang, Sheng-Chao, additional, Li, Lei, additional, Huang, Shao-Rong, additional, Chen, Shu, additional, Meng, Ling-Yan, additional, Panneerselvam, Rajapandiyan, additional, Zhang, San-Jun, additional, Ren, Bin, additional, Yang, Zhi-Lin, additional, Li, Jian-Feng, additional, and Tian, Zhong-Qun, additional

Published
2015
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28. Heterogeneous double populations based hybrid genetic algorithm design for training feedforward neural networks

Author

Rong He, Li Feng Zhang, and Meng Ling Yan

Subjects
Nonlinear system, Artificial neural network, Computer science, Generalization, business.industry, Genetic algorithm, Feedforward neural network, Context (language use), Genetic representation, Artificial intelligence, business, Least squares
Abstract

Genetic algorithms (GA) has been extensively applied to address the shortcomings of gradient based leaning methods in training feedforward neural networks (NN). However, the complicated properties of NN training, such as context dependence problem between neurons and permutation problem of genetic representation, will cause difficulties in efficiently implementing conventional GAs. In the present study, a novel hybrid GA design is proposed to overcome these problems. First, for the sake of eliminating the context dependence, the new method adopts GA and least squares estimator to separately optimize the neurons in hidden and output layers. Second, in order to completely avoid the permutation problem, the proposed GA design employs two heterogeneous populations that evolve in company but respectively learn the optimal combinations and parameters of hidden neuron. Finally, experimental studies encouragingly show that, in comparison with five well-known conventional approaches, the new training method displays a much better approximation and generalization capabilities in nonlinear static and dynamic modeling, especially for the observed signals corrupted with large measurement noises.

Published
2012

29. Electrochemical Tip-Enhanced Raman Spectroscopy

Author

Zeng, Zhi-Cong, primary, Huang, Sheng-Chao, additional, Wu, De-Yin, additional, Meng, Ling-Yan, additional, Li, Mao-Hua, additional, Huang, Teng-Xiang, additional, Zhong, Jin-Hui, additional, Wang, Xiang, additional, Yang, Zhi-Lin, additional, and Ren, Bin, additional

Published
2015
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32. Cellulose-based Nanocarriers as Platforms for Cancer Therapy

Author

Meng, Ling-Yan, Wang, Bin, Ma, Ming-Guo, and Zhu, Jie-Fang

Abstract

Cellulose is an important environmentally-friendly renewable polymer on the earth. Cellulose has been widely used as feedstocks for the synthesis of biomaterials, biofuels and biochemicals. Recently, cellulose and cellulose derivatives have received intense attention in biomedical applications, such as tissue engineering, scaffold, artificial blood vessel, skin grafts, artificial skin, drug carrier, and chronic skin diseases, many of which are somehow related to cancer therapy. In this mini-review, we focus on the up-to-date development of cellulosebased nanocarriers used for cancer therapy. Various cellulose-based nanocarriers such as bacterial cellulose (BC), cellulose acetate, microcrystalline cellulose, carboxymethyl cellulose, cellulose nanocrystals, cellulose nanofibrills, etc, are reviewed in terms of being used in drug delivery systems for cancer treatment. Different strategies for the synthesis of cellulose-based nanocarriers are summarized. Special attention is paid on the structure and properties of cellulose-based drug carriers for cancer therapy via some representative examples. Finally, the problems and future developments of these promising polymeric nanocarriers are raised and proposed.

Published
2017
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37. Heterogeneous double populations based hybrid genetic algorithm design for training feedforward neural networks.

Author

Zhang, Li Feng, Rong He, and Meng Ling Yan

Abstract

Genetic algorithms (GA) has been extensively applied to address the shortcomings of gradient based leaning methods in training feedforward neural networks (NN). However, the complicated properties of NN training, such as context dependence problem between neurons and permutation problem of genetic representation, will cause difficulties in efficiently implementing conventional GAs. In the present study, a novel hybrid GA design is proposed to overcome these problems. First, for the sake of eliminating the context dependence, the new method adopts GA and least squares estimator to separately optimize the neurons in hidden and output layers. Second, in order to completely avoid the permutation problem, the proposed GA design employs two heterogeneous populations that evolve in company but respectively learn the optimal combinations and parameters of hidden neuron. Finally, experimental studies encouragingly show that, in comparison with five well-known conventional approaches, the new training method displays a much better approximation and generalization capabilities in nonlinear static and dynamic modeling, especially for the observed signals corrupted with large measurement noises. [ABSTRACT FROM PUBLISHER]

Published
2012
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38. How To Light Special Hot Spots in Multiparticle–Film Configurations

Author

Chen, Shu, Meng, Ling-Yan, Shan, Hang-Yong, Li, Jian-Feng, Qian, Lihua, Williams, Christopher T., Yang, Zhi-Lin, and Tian, Zhong-Qun

Abstract

The precise control over the locations of hot spots in a nanostructured ensemble is of great importance in plasmon-enhanced spectroscopy, chemical sensing, and super-resolution optical imaging. However, for multiparticle configurations over metal films that involve localized and propagating surface plasmon modes, the locations of hot spots are difficult to predict due to complex plasmon competition and synergistic effects. In this work, theoretical simulations based on multiparticle–film configurations predict that the locations of hot spots can be efficiently controlled in the particle–particle gaps, the particle–film junctions, or in both, by suppressing or promoting specific plasmonic coupling effects in specific wavelength ranges. These findings offer an avenue to obtain strong Raman signals from molecules situated on single crystal surfaces and simultaneously avoid signal interference from particle–particle gaps.

Published
2016
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39. Formation of a salsolinol-like compound, the neurotoxin, 1-acetyl-6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline, in a cellular model of hyperglycemia and a rat model of diabetes.

Author

Song DW, Xin N, Xie BJ, Li YJ, Meng LY, Li HM, Schläppi M, and Deng YL

Subjects
Animals, Diabetes Complications metabolism, Diabetes Complications pathology, Diabetes Mellitus, Experimental genetics, Diabetes Mellitus, Experimental metabolism, Diabetes Mellitus, Experimental pathology, Disease Models, Animal, Dopaminergic Neurons metabolism, Dopaminergic Neurons pathology, Glucose metabolism, Hydroxyl Radical metabolism, Hyperglycemia genetics, Hyperglycemia pathology, Isoquinolines chemistry, Neurotoxins chemistry, Parkinson Disease etiology, Parkinson Disease metabolism, Rats, Diabetes Complications genetics, Isoquinolines metabolism, Neurotoxins metabolism, Parkinson Disease genetics, Tetrahydroisoquinolines metabolism
Abstract

There are statistical data indicating that diabetes is a risk factor for Parkinson's disease (PD). Methylglyoxal (MG), a biologically reactive byproduct of glucose metabolism, the levels of which have been shown to be increase in diabetes, reacts with dopamine to form 1-acetyl-6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline (ADTIQ); this formation may provide further insight into the connection between PD and diabetes. In this study, we investigated the role of ADTIQ in these two diseases to determine in an aim to enhance our understanding of the link between PD and diabetes. To this end, a cell model of hyperglycemia and a rat model of diabetes were established. In the cell model of hyperglycemia, compared with the control group, the elevated glucose levels promoted free hydroxyl radical formation (p<0.01). An ADTIQ assay was successfully developed and ADTIQ levels were detected and quantified. The levels of its precursors, MG and dopamine (DA), were determined in both the cell model of hyperglycemia and the rat model of diabetes. The proteins related to glucose metabolism were also assayed. Compared with the control group, ADTIQ and MG levels were significantly elevated not only in the cell model of hyperglycemia, but also in the brains of rats with diabetes (p<0.01). Seven key enzymes from the glycolytic pathway were found to be significantly more abundant in the brains of rats with diabetes. Moreover, it was found that adenosine triphosphate (ATP) synthase and superoxide dismutase (SOD) expression levels were markedly decreased in the rats with diabetes compared with the control group. Therefore, ADTIQ expression levels were found to be elevated under hyperglycemic conditions. The results reported herein demonstrate that ADTIQ, which is derived from MG, the levels of which are increased in diabetes, may serve as a neurotoxin to dopaminergic neurons, eventually leading to PD.

Published
2014
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