Formation of acrylamide in microwave-roasted sorghum and associated dietary risk

The effect of microwave roasting parameters (300, 450 and 600 W; 5, 10 and 15 min) on acrylamide content in sorghum grain was determined using High Pressure Liquid Chromatography (HPLC)-photo diode array (PDA) detector coupled with C-18 column. Samples roasted at 300 and 450 W did not possess acrylamide, whereas 600 W (15 min) favoured formation of 2740.19 µg/kg of acrylamide, levels far exceeding the defined European Union (EU) limits. The chronic daily intake (CDI) for acrylamide through consumption of such grain flour was 3.25–9.5-fold higher to Joint FAO/WHO Expert Committee on Food Additives (JECFA) defined high exposure limits. The margin of exposure (MOE) values ranged from 4.3 to 12.76 and from 11.07 to 32.27 for neoplastic and neurological effects, respectively, demonstrating high exposure and serious health concerns associated with dietary intake of this toxicant. This study assesses the risk for the Indian population and highlights the importance of optimising process parameters for food product to minimise such exposure risks.     

基于多通道的ICU脑血管疾病死亡风险预测模型

死亡风险预测指根据病人临床体征监测数据来预测未来一段时间的死亡风险。对于ICU病患，通过死亡风险预测可以有针对性地对病人做出临床诊断，以及合理安排有限的医疗资源。基于临床使用的MEWS和Glasgow昏迷评分量表，针对ICU病人临床监测的17项生理参数，提出一种基于多通道的ICU脑血管疾病死亡风险预测模型。引入多通道概念应用于BiLSTM模型，用于突出每个生理参数对死亡风险预测的作用。采用Attention机制用于提高模型预测精度。实验数据来自MIMIC [Ⅲ]数据库，从中提取3?080位脑血管疾病患者的16?260条记录用于此次研究，除了六组超参数实验之外，将所提模型与LSTM、Multichannel-BiLSTM、逻辑回归（logistic regression）和支持向量机（support vector machine, SVM）四种模型进行了对比分析，准确率Accuracy、灵敏度Sensitive、特异性Specificity、AUC-ROC和AUC-PRC作为评价指标，实验结果表明，所提模型性能优于其他模型，AUC值达到94.3%。     

Effects of upholstery materials on the burning behavior of real‐scale residential upholstered furniture mock‐ups

Fire spread and growth on real‐scale four cushion mock‐ups of residential upholstered furniture (RUF) were investigated with the goal of identifying whether changes in five classes of materials (barrier, flexible polyurethane foam, polyester fiber wrap, upholstery fabric, and sewing thread), referred to as factors, resulted in statistically significant changes in burning behavior. A fractional factorial experimental design plus practical considerations yielded a test matrix with 20 material combinations. Experiments were repeated a minimum of two times. Measurements included fire spread rates derived from video recordings and heat release rates (HRRs). A total of 13 experimental parameters (3 based on the videos and 10 on the HRR results), referred to as responses, characterized the measurements. Statistical analyses based on Main Effects Plots (main effects) and Block Plots (main effects and factor interactions) were used. The results showed that three of the factors resulted in statistically significant effects on varying numbers of the 13 responses. The Barrier and Fabric factors had the strongest main effects with roughly comparable magnitudes. Foam was statistically significant for fewer of the responses and its overall strength was weaker than for Barrier and Fabric. No statistically significant main effects were identified for Wrap or Thread. Multiple two‐term interactions between factors were identified as being statistically significant. The Barrier*Fabric interaction resulted in the highest number of and strongest statistically significant effects. The existence of two‐term interactions means that it will be necessary to consider their effects in approaches designed to predict the burning behavior of RUF.     

Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Prediction of mode I fracture toughness (K_IC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression (LMR) and gene expression programming (GEP) methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and elastic modulus (E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets. Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of K_IC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) were utilized herein. In the case of testing datasets, the values of R², RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156, respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R² value and lower errors.     

Trade-off study between risk and benefit in safety devices for hydrogen refueling stations using a dynamic physical model

As hydrogen refueling stations become increasingly common, it is clear that a high level of economic efficiency and safety is crucial to promoting their use. One way to reduce costs is to use a simple orifice instead of an excess flow valve, which Japanese safety regulations have identified as a safety device. However, there is concern about its effect on refueling time and on risk due to hydrogen leakage. To clarify the effect, we did a study of model-based refueling time evaluation and quantitative risk assessment for a typical refueling station. This study showed that an orifice is an effective alternative safety device. The increase in refueling time was less than 10%, based on simulations using a dynamic physical model of the station. Neither was there a significant difference in the risk between a configuration with excess flow valves and one with an orifice.     

乌兰矿采矿现状及技术应对方案

本文分析了乌兰矿投产前期采矿现状及存在的主要问题,针对该矿所处蒙古国经济落后、投资风险大的现实状况,为避免生产中断、规避投资风险,早日回收前期投资考虑,采取了双斜坡道开拓、全尾胶结充填、高端壁空场嗣后充填采矿、多中段组合式连续开采等系列技术应对方案。大大降低了一次性投资规模及投资风险,前期投资得以快速回笼的同时,矿山产能也充分释放,确保了矿山的持续稳定,取得了较好的经济和社会效益。为海外地下近地表矿体开采矿山规避投资风险提供了很好的技术方案借鉴。     

Fire safe,sustainable loose furnishing

The aim of this exploratory study has been to investigate the fire properties and environmental aspects of different upholstery material combinations, mainly for domestic applications. An analysis of the sustainability and circularity of selected textiles, along with lifecycle assessment, is used to qualitatively evaluate materials from an environmental perspective. The cone calorimeter was the primary tool used to screen 20 different material combinations from a fire performance perspective. It was found that textile covers of conventional fibres such as wool, cotton and polyester, can be improved by blending them with fire resistant speciality fibres. A new three‐dimensional web structure has been examined as an alternative padding material, showing preliminary promising fire properties with regard to ignition time, heat release rates and smoke production.     

Nacre-Inspired,Liquid Metal-Based Ultrasensitive Electronic Skin by Spatially Regulated Cracking Strategy

The realization of liquid metal-based wearable systems will be a milestone toward high-performance, integrated electronic skin. However, despite the revolutionary progress achieved in many other components of electronic skin, liquid metal-based flexible sensors still suffer from poor sensitivity due to the insufficient resistance change of liquid metal to deformation. Herein, a nacre-inspired architecture composed of a biphasic pattern (liquid metal with Cr/Cu underlayer) as “bricks” and strain-sensitive Ag film as “mortar” is developed, which breaks the long-standing sensitivity bottleneck of liquid metal-based electronic skin. With 2 orders of magnitude of sensitivity amplification while maintaining wide (>85%) working range, for the first time, liquid metal-based strain sensors rival the state-of-art counterparts. This liquid metal composite features spatially regulated cracking behavior. On the one hand, hard Cr cells locally modulate the strain distribution, which avoids premature cut-through cracks and prolongs the defect propagation in the adjacent Ag film. On the other hand, the separated liquid metal cells prevent unfavorable continuous liquid-metal paths and create crack-free regions during strain. Demonstrated in diverse scenarios, the proposed design concept may spark more applications of ultrasensitive liquid metal-based electronic skins, and reveals a pathway for sensor development via crack engineering.     

A Voltage Control Method for a Distributed Line Feeder by means of Multiagent Approach

This paper proposes a method for the coordinated control of power factor by means of a multiagent approach. The proposed multiagent system consists of two types of agent: single feeder agent (F_AG) and bus agent (B_AG). In the proposed system, an F_AG plays as an important role, which decides the power factors of all distributed generators by executing the load flow calculations repeatedly. The voltage control strategies are implemented as the class definition of Java into the system. In order to verify the performance of the proposed method, it has been applied to a typical distribution model system. The simulation results show that the system is able to control very violent fluctuation of the demands and the photovoltaic (PV) generations.