Efficient Denoising Framework for Mammogram Images with a New Impulse Detector and Non-Local Means

Objective: The survival rates of breast cancer are increasing as screening and diagnosis improve. The removal of noise is revealed to be a significant step for automatic - computer aided detection (CAD) of microcalcification in digital mammography. Methods: In this paper, a combined approach for eradicating impulse noise from digital mammograms is proposed. The process is achieved in two stages, detection of noise followed by filtering of noise. The detection of noise is carried out by using Modified Robust Outlyingness Ratio (mROR) trailed by an extended NL (Non-Local)-means filter for filtering mechanism. Results: According to the value of mROR, all pixels in mammogram images are divided into four distinct groups. In each cluster, many decision rules are then applied for detecting the impulse noise. Filtering is done with NL-means filter by providing a reference mammogram image. Conclusion: The comparative analysis and evaluated results are compared with some existing filters which indicate that the proposed structure outperforms the analysed result of others.     

模糊物元模型评价当归不同提取部位的活血作用

目的：筛选当归活血的有效部位;探讨模糊物元模型在当归不同提取部位活血效果综合评价中的应用。方法：以皮下注射盐酸肾上腺素结合冰水浴建立急性大鼠血瘀模型,通过检测血液流变学及凝血4项[凝血酶原时间（PT）,活化部分凝血活酶时间（APTT）和凝血酶时间（TT）,纤维蛋白原含量（FIB）]指标,评价当归不同提取部位的活血效果;运用基于变异系数权重的模糊物元模型对活血的效果进行综合评价。结果：部位1~7均能降低大鼠全血黏度和血浆黏度,部位2能明显延长PT、TT时间（P<0.05）。反映活血总效应的最大贴进度值为0.527（70%乙醇浸渍部位）。结论：70%乙醇浸渍部位为当归活血的有效部位;基于变异系数权重的模糊物元模型可客观、准确地评价当归不同提取部位活血的综合效果。     

Using leading and lagging indicators to select safe contractors at the prequalification stage of construction projects

The contractor-selection decision at the prequalification stage is critical to the project success. An insufficient prediction of contractors’ safety capacities using only lagging indicators may hinder the continuous improvement of safety performance in the construction industry. This research enhanced construction management and practices by proposing a comprehensive safe contractor selection model which integrated both leading and lagging indicators. First, a set of leading and lagging safety indicators were identified based on literature review and expert opinions. Then, the grey correlation analysis (GCA) was utilized to assign weights to individual indicators. We found that management commitment, safety training and education, safety risk management, and safety rules and procedures were four most influential factors to the safety performance of contractors. In addition, the fuzzy technique of ordering preference by similarity to ideal solution (Fuzzy TOPSIS) was used to condense individual indicators and create a composite safety performance indicator (c-SPI). Finally, the feasibility of the decision support tool for safe contractor selection was verified using a real-case railway construction project.     

基于药品全生命周期的中药监管模型的构建研究

随着2020版《药品管理法》正式增设药品上市许可持有人（marketing authorization holder,MAH）制度、药品监管科学行动计划以及《关于结束中药配方颗粒试点工作的公告》的正式实施,标志着中药正式进入全生命周期监管时代。当前,我国尚未能建立起一个符合中药规律和发展特点的全生命周期中药监管体系,中药产品疗效不稳定,我国的中医药产品安全性和使用有效性难以取得国际市场的重视和认可。因此,迫切需要建立一套全生命周期的中药监管体系,加强对中药的科学监管来推动其高质量发展。结合我国中药全生命周期实际监管中存在的问题,提出研制、生产、流通、使用环节指标,构建科学的中药全生命周期监管的评价体系,采用基于模糊群决策的评价方法科学评价各因素,以期规范中药全生命周期监管,促进中药质量标准与国际接轨。     

Application of an extreme learning machine network with particle swarm optimization in syndrome classification of primary liver cancer

ObjectiveBy optimizing the extreme learning machine network with particle swarm optimization, we established a syndrome classification and prediction model for primary liver cancer (PLC), classified and predicted the syndrome diagnosis of medical record data for PLC and compared and analyzed the prediction results with different algorithms and the clinical diagnosis results. This paper provides modern technical support for clinical diagnosis and treatment, and improves the objectivity, accuracy and rigor of the classification of traditional Chinese medicine (TCM) syndromes.MethodsFrom three top-level TCM hospitals in Nanchang, 10,602 electronic medical records from patients with PLC were collected, dating from January 2009 to May 2020. We removed the electronic medical records of 542 cases of syndromes and adopted the cross-validation method in the remaining 10,060 electronic medical records, which were randomly divided into a training set and a test set. Based on fuzzy mathematics theory, we quantified the syndrome-related factors of TCM symptoms and signs, and information from the TCM four diagnostic methods. Next, using an extreme learning machine network with particle swarm optimization, we constructed a neural network syndrome classification and prediction model that used “TCM symptoms + signs + tongue diagnosis information + pulse diagnosis information” as input, and PLC syndrome as output. This approach was used to mine the nonlinear relationship between clinical data in electronic medical records and different syndrome types. The accuracy rate of classification was used to compare this model to other machine learning classification models.ResultsThe classification accuracy rate of the model developed here was 86.26%. The classification accuracy rates of models using support vector machine and Bayesian networks were 82.79% and 85.84%, respectively. The classification accuracy rates of the models for all syndromes in this paper were between 82.15% and 93.82%.ConclusionCompared with the case of data processed using traditional binary inputs, the experiment shows that the medical record data processed by fuzzy mathematics was more accurate, and closer to clinical findings. In addition, the model developed here was more refined, more accurate, and quicker than other classification models. This model provides reliable diagnosis for clinical treatment of PLC and a method to study of the rules of syndrome differentiation and treatment in TCM.     

Preschool predictors of mathematics in first grade children with autism spectrum disorder

Up till now, research evidence on the mathematical abilities of children with autism spectrum disorder (ASD) has been scarce and provided mixed results. The current study examined the predictive value of five early numerical competencies for four domains of mathematics in first grade. Thirty-three high-functioning children with ASD were followed up from preschool to first grade and compared with 54 typically developing children, as well as with normed samples in first grade. Five early numerical competencies were tested in preschool (5–6 years): verbal subitizing, counting, magnitude comparison, estimation, and arithmetic operations. Four domains of mathematics were used as outcome variables in first grade (6–7 years): procedural calculation, number fact retrieval, word/language problems, and time-related competences. Children with ASD showed similar early numerical competencies at preschool age as typically developing children. Moreover, they scored average on number fact retrieval and time-related competences and higher on procedural calculation and word/language problems compared to the normed population in first grade. When predicting first grade mathematics performance in children with ASD, both verbal subitizing and counting seemed to be important to evaluate at preschool age. Verbal subitizing had a higher predictive value in children with ASD than in typically developing children. Whereas verbal subitizing was predictive for procedural calculation, number fact retrieval, and word/language problems, counting was predictive for procedural calculation and, to a lesser extent, number fact retrieval. Implications and directions for future research are discussed.     

Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs)

In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature.     

蛋白亚细胞定位的预测方法研究

预测蛋白质的亚细胞定位信息对于了解其功能有重要的意义.选择氨基酸组成、氨基酸对组成、位置特异性打分矩阵三种分类特征以及模糊k近邻、支持向量机两种预测方法,分别进行了测试.对预测结果的分析显示,位置特异性打分矩阵可以提高对不同亚细胞器的可区分性;而支持向量机可以更好地利用位置特刎异性打分矩阵特征进行预测.使用氨基酸组成和位置特异性打分矩阵两种特征,并结合支持向量机,是一种有效的亚细胞定位预测方法.