Simultaneous determination of ultra trace amounts of lead and cadmium in food samples by adsorptive stripping voltammetry

A selective and sensitive method for simultaneous determination of lead and cadmium by adsorptive differential pulse cathodic stripping voltammetry is presented. The method is based on adsorptive accumulation of the complexes of Pb (II) and Cd (II) ions with 2-mercaptobenzothiazole onto hanging mercury drop electrode (HMDE), followed by the reduction of the adsorbed species by differential pulse cathodic stripping voltammetry. Optimal conditions were obtained at pH 8.0, 2-mercaptobenzothiazole concentration of 1.0 × 10⁻⁴ M, the accumulation potential of −0.4 V (vs. Ag/AgCl), the accumulation time of 160 s, and the scan rate of 100 mV/s. Under optimised conditions, linear calibration curves were established for the concentration of Pb (II) and Cd (II) in the range of 0.5–70 and 0.2–30 ng/ml, respectively, with detection limit of 0.017 ng/ml for Pb (II) and 0.01 ng/ml for Cd (II). The procedure was successfully applied to the simultaneous determination of both ions in food samples (rice, soya and sugar).     

Occupational exposure and health risks of volatile organic compounds of hotel housekeepers: Field measurements of exposure and health risks

Hotel housekeepers represent a large, low-income, predominantly minority, and high-risk workforce. Little is known about their exposure to chemicals, including volatile organic compounds (VOCs). This study evaluates VOC exposures of housekeepers, sources and factors affecting VOC levels, and provides preliminary estimates of VOC-related health risks. We utilized indoor and personal sampling at two hotels, assessed ventilation, and characterized the VOC composition of cleaning agents. Personal sampling of hotel staff showed a total target VOC concentration of 57 ± 36 µg/m³ (mean ± SD), about twice that of indoor samples. VOCs of greatest health significance included chloroform and formaldehyde. Several workers had exposure to alkanes that could cause non-cancer effects. VOC levels were negatively correlated with estimated air change rates. The composition and concentrations of the tested products and air samples helped identify possible emission sources, which included building sources (for formaldehyde), disinfection by-products in the laundry room, and cleaning products. VOC levels and the derived health risks in this study were at the lower range found in the US buildings. The excess lifetime cancer risk (average of 4.1 × 10⁻⁵) still indicates a need to lower exposure by reducing or removing toxic constituents, especially formaldehyde, or by increasing ventilation rates.     

Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.     

Robustness of asymptotic accelerated life test plans to small‐sample settings

Much of the statistical literature on optimal test planning for accelerated life testing utilize asymptotic methods to derive optimal test plans. While sufficient effort is made to assess the robustness of these test plans to the choice of design parameters and distribution assumptions, there is very little literature on the performance of asymptotic test plans relative to small samples (on the order of 10‐15 samples). An alternative concern is that the asymptotic test plans may not necessarily be the true “optimal” test plan for a given sample size. The purpose of this research is to present exact or “near‐exact” methods for developing test plans and compare the performance of these test plans with corresponding asymptotic test plans in small‐sample settings. The optimal location of design points and sample allocation is determined using each method for lognormal and Weibull lifetime distributions with both complete and Type 1 right‐censored data under two selected acceleration factor models. The investigations reveal that asymptotic test plans tend to corroborate quite well with exact test plans and thus are suitably robust to small‐sample settings in terms of optimal variance.     

Residue analysis of 500 high priority pesticides: Better by GC–MS or LC–MS/MS?

This overview evaluates the capabilities of mass spectrometry (MS) in combination with gas chromatography (GC) and liquid chromatography (LC) for the determination of a multitude of pesticides. The selection of pesticides for this assessment is based on the status of production, the existence of regulations on maximum residue levels in food, and the frequency of residue detection. GC-MS with electron impact (EI) ionization and the combination of LC with tandem mass spectrometers (LC-MS/MS) using electrospray ionization (ESI) are identified as techniques most often applied in multi-residue methods for pesticides at present. Therefore, applicability and sensitivity obtained with GC-EI-MS and LC-ESI-MS/MS is individually compared for each of the selected pesticides. Only for one substance class only, the organochlorine pesticides, GC-MS achieves better performance. For all other classes of pesticides, the assessment shows a wider scope and better sensitivity if detection is based on LC-MS.     

电感耦合等离子体质谱法测定地质样品中的镉

采用电感耦合等离子体质谱法测定岩石、土壤、水系沉积物等地质样品中微量镉,方法简便?快速。Cd的检出限为0.0036μg/g,相对标准偏差(n=10)为0.71%,标准Cd加入回收率为94%~106%。该方法用国家一级标准物质验证,结果与推荐值相符。     

复杂系统可靠度置信区间估计

针对复杂系统可靠性试验非常少甚至没有做的情况，提出了基于单元信息进行可靠性综合的方法。该方法不需假设系统或单元产品的寿命服从某一分布，减少了因寿命分布选择不当所造成的可靠性和方差的误差。在获得单元可靠性的均值和方差的基础上，利用系统可以分解为单一的串联或并联关系，通过逐步综合获得复杂系统的可靠性均值和方差估计值。利用系统信息熵原理，将部件的试验数据折合为系统的试验数据，获得系统的子样数。由此提出了小子样下的系统可靠性置信区间估计新方法，该方法只假设系统可靠性估计服从正态或对数正态分布。新方法使用限制少，计算简单，适用于系统数据较少的可靠性分析.     

面向一致性样本的属性约简

作为粗糙集理论的一个核心内容,属性约简致力于根据给定的约束条件删除数据中的冗余属性。基于贪心策略的启发式算法是求解约简的一种有效手段,这一手段通常使用数据中的全部样本来度量属性的重要度从而进一步得到约简子集。但实际上,不同样本对于属性重要度计算的贡献是不同的,有些样本对重要度贡献不高甚至几乎没有贡献,且当数据中的样本数过大时,利用全部样本进行约简求解会使得时间消耗过大而难以接受。为了解决这一问题,提出了一种基于一致性样本的属性约简策略。具体算法大致由3个步骤组成,首先,将满足一致性原则的样本挑选出来;其次,将这些选中的样本组成新的决策系统;最后,利用启发式框架在新的决策系统中求解约简。实验结果表明:与基于聚类采样的属性约简算法相比,所提方法能够提供更高的分类精度。     

Parametric design-based multi-objective optimisation for high-pressure turbine disc

Mass and radial deformation are of great importance for a high-pressure turbine disc (HPTD). However, computational cost of computer-aided engineering (CAE) is too high to optimise the mutually restricted objectives. A parameterisation-based method is proposed to speed the optimisation process of HPTD: ‘body-flange’-based parametric template is used to generate CAE samples; noise-based virtual samples are implemented to enlarge the training set, a cost-effective neural network is used as fitness function of non-dominated sorting genetic algorithm-II for optimisation whose initial population is the combination of different sample sets. Experiment results show that the proposed data-driven framework reduces the engineering difficulty of multi-objective optimisation, and it has high popularisation value for optimisation of other complex products.