An automated patient monitoring using discrete‐time wireless sensor networks

In recent years, Internet of Things (IoT) devices are used for remote health monitoring. For remotely monitoring a patient, only the health information at different time points are not sufficient; predicted values of biomarkers (for some future time points) are also important. In this article, we propose a powerful statistical model for an efficient dynamic patient monitoring using wireless sensor nodes through Bayesian Learning (BL). We consider the setting where a set of correlated biomarkers are measured from a patient through wireless sensors, but the sensors only report the ordinal outcomes (say, good, fair, high, or very high) to the sink based on some prefixed thresholds. The challenge is to use the ordinal outcomes for monitoring and predicting the health status of the patient under consideration. We propose a linear mixed model where interbiomarker correlations and intrabiomarker dependence are modeled simultaneously. The estimated and the predicted values of the biomarkers are transferred over the internet so that health care providers and the family members of the patient can remotely monitor the patient. Extensive simulation studies are performed to assess practical usefulness of our proposed joint model, and the performance of the proposed joint model is compared to that of some other traditional models used in the literature.     

Association of Dietary Phytosterols with Cardiovascular Disease Biomarkers in Humans

Cardiovascular disease (CVD) is a leading cause of death worldwide. Elevated concentrations of serum total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) are major lipid biomarkers that contribute to the risk of CVD. Phytosterols well known for their cholesterol-lowering ability, are non-nutritive compounds that are naturally found in plant-based foods and can be classified into plant sterols and plant stanols. Numerous clinical trials demonstrated that 2 g phytosterols per day have LDL-C lowering efficacy ranges of 8–10%. Some observational studies also showed an inverse association between phytosterols and LDL-C reduction. Beyond the cholesterol-lowering beneficial effects of phytosterols, the association of phytosterols with CVD risk events such as coronary artery disease and premature atherosclerosis in sitosterolemia patients have also been reported. Furthermore, there is an increasing demand to determine the association of circulating phytosterols with vascular health biomarkers such as arterial stiffness biomarkers. Therefore, this review aims to examine the ability of phytosterols for CVD risk prevention by reviewing the current data that looks at the association between dietary phytosterols intake and serum lipid biomarkers, and the impact of circulating phytosterols level on vascular health biomarkers. The clinical studies in which the impact of phytosterols on vascular function is investigated show minor but beneficial phytosterols effects over vascular health. The aforementioned vascular health biomarkers are pulse wave velocity, augmentation index, and arterial blood pressure. The current review will serve to begin to address the research gap that exists between the association of dietary phytosterols with CVD risk biomarkers.     

基于WiMAX通信网络的电源监控系统的构建

分析了全球微波接入互存(WiMAX)的技术特点和算法复杂度,探讨了其在电源监控系统中的应用,提出了基于Inter WiMAX Connection 2250技术的远程电源监控系统。在讨论WiMAX物理层算法的基础上,确定了以正交频分多址(OFDMA)模式为主体的物理层通信协议,提高了信道的利用率,抑制了码间干扰,为构建电源监控系统打下了良好的基础。     

Dietary advanced glycation end‐products: Perspectives linking food processing with health implications

Dietary advanced glycation end products (dAGEs) are complex and heterogeneous compounds derived from nonenzymatic glycation reactions during industrial processing and home cooking. There is mounting evidence showing that dAGEs are closely associated with various chronic diseases, where the absorbed dAGEs fuel the biological AGEs pool to exhibit noxious effects on human health. Currently, due to the uncertain bioavailability and rapid renal clearance of dAGEs, the relationship between dAGEs and biological AGEs remains debatable. In this review, we provide the most updated information on dAGEs including their generation in processed foods, analytical and characterization techniques, metabolic fates, interaction with AGE receptors, implications on human health and reducing strategies. Available evidence demonstrating a relevance between dAGEs and food allergy is also included. AGEs are ubiquitous in foods and their contents largely depend on the reactivity of carbonyl and amino groups, along with surrounding condition mainly pH and heating procedures. Once being digested and absorbed into the circulation, two separate pathways can be involved in the deleterious effects of dAGEs: an AGE receptor‐dependent way to stimulate cell signals, and an AGE receptor‐independent way to dysregulate proteins via forming complexes. Inhibition of AGEs formation during food processing and reduction in the diet are two potent approaches to restrict health‐hazardous dAGEs. To elucidate the biological role of dAGEs toward human health, the following significant perspectives are raised: molecular size and complexity of dAGEs; interactions between unabsorbed dAGEs and gut microbiota; and roles played by concomitant compounds in the heat‐processed foods.     

高应力区沿空留巷围岩现状监测研究与分析

陈四楼矿计划实施沿空留巷的2802运输巷，断层较多，直接顶厚度较大，巷道矿压显现较为明显。针对顶板下沉、两帮外鼓、顶板淋水较大、巷道顶板受压破碎、金属网锈蚀严重、压脱锚杆较多、两帮位移较大等现象，提出在高应力区采用矿压监测的方法，通过对巷道变形现状进行的监测分析，对巷道破坏变形程度有了清晰的认识，对造成巷道破坏变形原因和影响因素有了详细的分析，根据注浆加固可改善围岩结构，提高围岩承载能力及锚杆、锚索锚固性能，是一种较为有效的围岩控制措施，确定了需要采用注浆加固的方式对进行巷道围岩破坏变形治理，并根据现场实际情况对注浆材料的性能提出了要求。     

Detection of Multiple Blockages in Parallelized Microreactors

In a plant consisting of parallelized microreactors (MRs), the product quality is lowered because of a lack of flow uniformity among them when blockage occurs. It is not practical to install sensors in every MR from the viewpoint of cost when detecting the blocked MRs. In the previous study, the multiple blockage detection (MBD) method using a small number of sensors was proposed, but its performance became low when the number of sensors decreased. Here, the conventional algorithm for MBD is improved by considering the process behavior on blockage occurrence, and the effectiveness of the improved algorithm is demonstrated through a numerical case study. The effects of flow distributor types and sensor types on the MBD performance are numerically investigated.     

Flow state monitoring of gas-water two-phase flow using multi-Gaussian mixture model based on canonical variate analysis

As a highly complex and time-varying process, gas-water two-phase flow is commonly encountered in industries. It has a variety of typical flow states and transition flow states. Accurate identification and monitoring of flow states is not only beneficial to further study of two-phase flow but also helpful for stable operation and economic efficiency of process industry. Combining canonical variate analysis (CVA) and Gaussian mixture model (GMM), a strategy called multi-CVA-GMM is proposed for flow state monitoring in gas-water two-phase flow. CVA is used to extract flow state features from the perspective of correlation between historical data and future data, which solves the cross correlation and temporal correlation of multi-sensor measurement data. GMM calculates the possibility that the current flow state belongs to each typical flow pattern and judges the current flow state by probability indicators. It is conducive to follow-up use of Bayesian inference probability and Mahalanobis distance-based (BID) indicator for flow state monitoring, which avoids repeated traversal of multiple CVA-GMM models and improves the efficiency of the monitoring process. The probability indicators can also be used to analyze transition flow states. The method combining the probabilistic idea of GMM with the deterministic idea of multimodal modeling can accurately identify the current flow state and effectively monitor the evolution of flow state. The multi-CVA-GMM method is validated by using the measured data of the horizontal flow loop of gas-water two-phase flow experimental facility, and its effectiveness is proved.     

Skeletal Muscle Health and Cognitive Function: A Narrative Review

Sarcopenia is the loss of skeletal muscle mass and function with advancing age. It involves both complex genetic and modifiable risk factors, such as lack of exercise, malnutrition and reduced neurological drive. Cognitive decline refers to diminished or impaired mental and/or intellectual functioning. Contracting skeletal muscle is a major source of neurotrophic factors, including brain-derived neurotrophic factor, which regulate synapses in the brain. Furthermore, skeletal muscle activity has important immune and redox effects that modify brain function and reduce muscle catabolism. The identification of common risk factors and underlying mechanisms for sarcopenia and cognition may allow the development of targeted interventions that slow or reverse sarcopenia and also certain forms of cognitive decline. However, the links between cognition and skeletal muscle have not been elucidated fully. This review provides a critical appraisal of the literature on the relationship between skeletal muscle health and cognition. The literature suggests that sarcopenia and cognitive decline share pathophysiological pathways. Ageing plays a role in both skeletal muscle deterioration and cognitive decline. Furthermore, lifestyle risk factors, such as physical inactivity, poor diet and smoking, are common to both disorders, so their potential role in the muscle–brain relationship warrants investigation.