Data monitoring and sports injury prediction model based on embedded system and machine learning algorithm

Managing sports performance is very important in the sports industry. Performance, the executives, centers on boosting competitor execution and decreasing the danger of injury. Several factors contribute to these goals, including player health, emotional status, exercise load and physical intensity requirements. Generally speaking, injury prediction is an essential component of injury prevention, and successful identification of injury prediction is a primary indicator for effective prevention. The proposed Artificial Neural Network (ANN) objective is to develop and use early-doing ability and exercise load data to validate a hierarchical machine learning prediction system with accurate detection of player injuries. The physical and workload that requires detection of this early personalized damage can be avoided with specific help. The framework is used to test 21 soccer players’ sports information from various sources, including gathered and inside burden information, outside burden information, and review information. The entirety of this information is fused into the proposed framework to improve the exactness of harm expectation. This calculation distinguishes competitors in danger of injury, with their early intervention available.     

Landscaping of highway medians and roadway safety at unsignalized intersections

Well-planted and maintained landscaping can help reduce driving stress, provide better visual quality, and decrease over speeding, thus improving roadway safety. Florida Department of Transportation (FDOT) Standard Index (SI-546) is one of the more demanding standards in the U.S. for landscaping design criteria at highway medians near intersections. The purposes of this study were to (1) empirically evaluate the safety results of SI-546 at unsignalized intersections and (2) quantify the impacts of geometrics, traffic, and landscaping design features on total crashes and injury plus fatal crashes. The studied unsignalized intersections were divided into (1) those without median trees near intersections, (2) those with median trees near intersections that were compliant with SI-546, and (3) those with median trees near intersections that were non-compliant with SI-546. A total of 72 intersections were selected, for which five-year crash data from 2006–2010 were collected.The sites that were compliant with SI-546 showed the best safety performance in terms of the lowest crash counts and crash rates. Four crash predictive models—two for total crashes and two for injury crashes—were developed. The results indicated that improperly planted and maintained median trees near highway intersections can increase the total number of crashes and injury plus fatal crashes at a 90% confidence level; no significant difference could be found in crash rates between sites that were compliant with SI-546 and sites without trees. All other conditions remaining the same, an intersection with trees that was not compliant with SI-546 had 63% more crashes and almost doubled injury plus fatal crashes than those at intersections without trees. The study indicates that appropriate landscaping in highway medians near intersections can be an engineering technology that not only improves roadway environmental quality but also maintains intersection safety.     

Performance optimization of integrated resilience engineering and lean production principles

This paper conducts performance assessment from integrated resilience engineering (IRE) and lean production points of view. This is the first study that evaluates the impact of integrated resilience engineering (IRE) on lean production principles. Second, this study considers integrated impact of lean production by a unique intelligent algorithm. The proposed algorithm is composed of radial basis function (RBF), multi-layer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS). Moreover, the algorithm is capable of handling both crisp and fuzzy data due to the existence of intelligent approach. The proposed algorithm is equipped with verification and validation mechanism through conventional regression, statistical methods and data envelopment analysis. To demonstrate the applicability of the study, a real-world pipe manufacturer is considered as our case study. The results showed that “pull system” and “fault tolerant” among lean and IRE factors, respectively have been implemented inappropriately, while other factors are either suitably executed or ineffective.     

基于主成分和聚类分析评价国产不同批次肉豆蔻挥发油的质量

本文对16批次肉豆蔻样品的挥发油进行化学成分分析鉴定，对其共有化学成分采用主成分分析和聚类分析，从而对肉豆蔻挥发油进行综合质量评价。通过对其形状、气味、色泽等性状进行记录，采用水蒸气蒸馏法提取肉豆蔻挥发油，通过气相色谱-质谱联用色谱仪（GC-MS）所得挥发性成分，结合NIST08系列标准谱库、人工检索等方式对其进行鉴定分析，利用主成分分析和聚类分析对16批次肉豆蔻样品进行综合评价。16批次肉豆蔻多为卵圆形或椭圆形、香气浓烈、灰棕色；从肉豆蔻挥发油化学成分中共筛选出17个共有成分，经主成分分析概括为4个主成分，包含源信息量的86.26%；聚类分析结果显示，16批次肉豆蔻样品可聚为两大类，S15批次聚为一类，S1-S14，S16批次聚为一类；两种分析结果达成一致。通过主成分分析和聚类分析，同时结合性状品质与安全性分析，可以建立肉豆蔻挥发油的质量评价体系，为肉豆蔻挥发油的药食两用以及其他方面的进一步开发研究提供参考依据，该评价方法综合性强、全面性高。     

基于工艺优选的冠突曲霉发酵桑寄生茶防治非酒精性脂肪肝病的潜在机制研究

目的：建立冠突曲霉发酵桑寄生茶最佳工艺,探究冠突曲霉发酵桑寄生茶防治非酒精性脂肪肝病(nonalcoholic fatty liver disease,NAFLD)的潜在机制。方法：采用层次分析法(analytic hierarchy process,AHP),以主要化学成分和感官审评为评价指标,通过单因素实验和正交试验对冠突曲霉发酵桑寄生茶的加工工艺进行优化。通过“0.1%蛋黄粉+5 mmol·mL^-1 硫代乙酰胺”诱导斑马鱼幼鱼NAFLD模型,探究冠突曲霉发酵桑寄生茶对NAFLD的防治作用。通过超高效液相色谱串联-四级杆-飞行时间质谱技术(UPLC-Q-TOF-MS/MS)和网络药理学,筛选关键成分和核心靶点并进行分子对接。结果：最佳发酵工艺为含水量35%,渥堆时间3 h,菌液浓度1×10⁶ CFU·mL^-1 ,发酵时间7 d。冠突曲霉发酵桑寄生茶通过降低总胆固醇、甘油三酯、低密度脂蛋白胆固醇、谷草转氨酶、谷丙转氨酶达到防治NAFLD的效果(P<0.05)。通过UPLC-Q-TOF-MS/MS分析得到14个差异...     

Overview of pharmacological activities of Andrographis paniculata and its major compound andrographolide

Abstract

Andrographis paniculata (A. paniculata) is a medicinal plant traditionally used as anti-inflammation and anti-bacteria herb. Andrographolide, the major active component of A. paniculata, exhibits diverse pharmacological activities, including anti-inflammation, anti-cancer, anti-obesity, anti-diabetes, and other activities. In this article, we comprehensively review the therapeutic potential of A. paniculata and andrographolide focusing on the mechanisms of action and clinical application. We systemically discuss the structure-activity relationship of andrographolide and derivatives. Despite the various pharmacological activities and formula of A. paniculata and andrographolide, we propose further development of more structural derivatives of andrographolide with reduced toxicity and increased therapeutic efficacy is still needed for the clinical application of this ancient mighty herb and its major component.     

Triboelectric nanogenerators stimulated electroacupuncture (EA) treatment for promoting the functional recovery after spinal cord injury

Electroacupuncture (EA), as a particular type of electrostimulation treatment, has an extensive application in the field of biomedicine. Triboelectric nanogenerator (TENG) has sparked significant concern attributed to the high electrical output with low cost, whose electrical signals can also be directly usable for electrical stimulation. Here we propose a Chinese medicine EA treatment with the assistance of TENG technology, in which a bidirectional continuous current from a soft-contact freestanding rotary TENG (FR-TENG) is applied to two effective acupoints of rats by inserting electric needles. The TENG-driven EA treatment promotes the performance of gait 2 weeks after as well as the Basso–Beattie–Bresnahan (BBB) score. This elevation last as long as 4 weeks post injury. Moreover, the TENG-driven EA treatment enhances neuron survival in the ventral horn and inhibits astrocyte activation in the lesion site. Therefore, the TENG-driven EA treatment provides a significant neuroprotection effect on spinal cord contusion in rats. Our studies not only demonstrate the possibility of the TENG-driven EA treatment for traumatic central nervous system injuries but also provide an experimental basis for the prevention and treatment of diseases by traditional Chinese medicine treatment.     

Utilizing advances in correlation analysis for community structure detection

In the era of big data, some data records are interrelated with each other in many areas, such as marketing, management, health care, and education. These interrelated data can be more naturally represented as networks with nodes and edges. Inside this type of networks, there is usually a hidden community structure where each community represents a relatively independent functional module. Such hidden community structures are useful for many applications, such as word-of-mouth marketing, promoting decentralized social interactions inside organizations, and searching biological pathways related to various diseases. Therefore, how to detect hidden community structures becomes an important task with wide applications. Currently, modularity-based methods are widely-used among many existing community structure detection methods. They detect communities with more internal edges than expected under the null hypothesis of independence. Since research in correlation analysis also searches for patterns which occur more than expected under the null hypothesis of independence, this paper proposed a framework of changing the original modularity function according to different existing correlation functions in the correlation analysis research area. Such a framework can utilize not only the current but also the future potential research progresses in correlation analysis to advance community detection. In addition, a novel graphical analysis on different modified-modularity functions is conducted to analyze their different preferences, which are also validated by our evaluation on both real life and simulated networks. Our work to connect modularity-based methods with correlation analysis has several significant impacts on the community detection research and its applications to expert and intelligent systems. First, the research progress in correlation analysis can be utilized to define a more effective objective function in community detection for better detection results since different real-life applications might need communities with different resolutions. Second, any existing research progress for the modularity function, such as the Louvain method for speeding up the search and different extensions for overlapping community detection, can be applied in a similar way to the new objective function derived from existing correlation functions, because the new objective function is unified within one framework with the modularity function. Third, our framework opens a large unexplored area for the researchers interested in community detection. For example, what is the best heuristic search method for each different objective function? What are the characteristics of each objective function when applied to overlapping community detection? Among different extensions to overlapping community detection, which extension is better for each objective function?     

荔枝核研究进展及其质量标志物预测分析

我国是世界上第一大荔枝生产国,种植面积占全世界的80%以上,由于荔枝的种植量越来越大,荔枝核的产量也逐渐增加,但其往往被随意丢弃,不仅对环境造成污染,同时也造成了资源的浪费。荔枝核具有很好的药用价值,但在15年版《中国药典》中并未规定其质量控制标准。为了完善荔枝核质量标准,增强人们对荔枝核的保护意识,将在其化学成分及药理作用综述的基础上,结合质量标志物概念,基于化学特有成分、临床新用途、可测成分、传统药性功效、入血成分和配伍环境等几个方面对荔枝核质量标志物进行预测分析,以期为荔枝核质量评价及产品研发提供科学依据。