Structure of presented stimuli influences gazing behavior and choice

Well-structured stimuli presentation is essential in eye-tracking research to test predefined hypotheses reliably and to conduct relevant gazing behavior studies. Several bottom-up factors associated with stimuli presentation (such as stimuli orientation, size etc.) can influence gazing behavior. However, only a small number of scientific papers address these factors in a sensory and consumer science context and thus provide guidance to practitioners. The two presented eye-tracking studies on food images aimed at evaluating the effect of the bottom-up factors stimulus size, background of the picture, orientation of food product presentation, the evaluated products and the number of alternatives. Significant effects of product group were found in the case of all eye-movement parameters except time to first fixation and first fixation duration. In contrary, orientation significantly influenced only the time to first fixation and first fixation duration parameters. Stimulus size significantly increased fixation and dwell count, while background showed no significant effects. Furthermore, significant relationships were found between the number of presented images and eye-movement and decision time. Less time was needed in 2AFC (alternative forced choice test), 3AFC and 4AFC and significantly more time was needed to choose one alternative out of 7AFC and 8AFC. The results of the two studies show that the investigated bottom-up factors can significantly influence gazing behavior, and therefore need to be carefully considered when planning or comparing results of eye-tracking experiments.     

A novel low-latency DRAM based on the bitline-discharge rate

As well known by computer architects, the performance gap between the processor and the memory has been increasing over the years. This causes what is known as the memory wall. In order to alleviate the problem, a novel fast readout scheme is proposed in this article for the single-transistor single-capacitor dynamic random-access memory (1T-1C DRAM) cells. The proposed scheme works in the current domain in which the difference between the discharging rates of the bitline in the cases of ‘1’ and ‘0’ readings is detected. The proposed scheme is analysed quantitatively and compared with the conventional readout scheme. It is verified by simulation adopting the 45 nm CMOS Berkley predictive-technology model (BPTM) and shows 44 and 7.7% reductions in the average read-access and cycle times, respectively, as compared to the conventional readout scheme. It is also shown that the power is saved according to the proposed scheme if the probability of occurrence of ‘0’ storage exceeds 66.7%. This minimum value can be alleviated, however, at the expense of a smaller saving in the average read-access time. The impacts of process variations and technology scaling are also taken into account.     

Black hole entropy,the black hole information paradox,and time travel paradoxes from a new perspective

ABSTRACT

Relatively simple but apparently novel ways are proposed for viewing three related subjects: black hole entropy, the black hole information paradox, and time travel paradoxes. (1) Gibbons and Hawking have completely explained the origin of the entropy of all black holes, including physical black holes – nonextremal and in 3-dimensional space – if one can identify their Euclidean path integral with a true thermodynamic partition function (ultimately based on microstates). An example is provided of a theory containing this feature. (2) There is unitary quantum evolution with no loss of information if the detection of Hawking radiation is regarded as a measurement process within the Everett interpretation of quantum mechanics. (3) The paradoxes of time travel evaporate when exposed to the light of quantum physics (again within the Everett interpretation), with quantum fields properly described by a path integral over a topologically nontrivial but smooth manifold.     

Dynamic tensile mechanical properties and failure mode of zirconium diboride-silicon carbide ceramic composites

Dynamic indirect tension experiments were performed on zirconium diboride-silicon carbide (ZrB₂−20%SiC) ceramic. Flattened Brazilian disc specimens of ZrB₂−20%SiC were prepared to conduct dynamic tensile tests using the modified Split Hopkinson pressure bar system. The tensile experiments were completed at the range of loading rates from 7.53 to 74.71 GP s⁻¹. The tensile experimental results revealed that the zirconium diboride-silicon carbide ceramic composite is rate-sensitive in terms of the tensile strength and failure mode. The dynamic tensile strength increases linearly with the loading rate and changes from 195 MPa at 7.53 GP s⁻¹ to 654 MPa at 74.71 GP s⁻¹. Moreover, the dynamic tensile strength decreases with the increase in critical fracture time, which conforms to Tuler and Butcher's fracture criterion. In dynamic experiments, a high-speed camera was used to examine the tensile failure process, and fragments were collected to analyze the dynamic tensile failure mechanism. The tensile fracture mode of ZrB₂−20%SiC obviously showed the sensitivity of the loading rate. The fragment size of ZrB₂−20%SiC ceramic decreased but the quantity of fragments increased as the loading rate increased.     

基于多变量时间序列及向量自回归机器学习模型的水驱油藏产量预测方法

提出了一种基于多变量时间序列(MTS)及向量自回归(VAR)机器学习模型的水驱油藏产量预测方法,并进行了实例应用。该方法在井网分析的基础上通过MTS分析对注采井组数据进行优选,并将井组内不同采出井产油量及注入井注水量作为彼此相关的时间序列,通过建立VAR模型从多个时间序列中提取出相互作用规律,挖掘注采井间流量的依赖关系从而进行产量预测。水驱油藏历史生产数据分析结果表明,与数值模拟历史拟合结果相比,机器学习模型产量预测结果具有更高精度,同时不确定性分析提升了预测结果的安全性。通过脉冲响应分析对注入井的采油贡献量进行评价,可为注水开发方案调整提供理论指导。     

响应面法优化α-淀粉酶的酶反应体系

目的:采用单因素实验及响应面法优化α-淀粉酶的反应体系。方法:以淀粉为底物对象，以可溶性淀粉浓度、α-淀粉酶浓度、反应时间为考察因素，在单因素实验基础上，运用Box-behnken实验设计方法研究各因素及其交互作用对α-淀粉酶作用底物时的反应速度的影响。结果:建立α-淀粉酶酶反应体系的最佳反应条件为12.0 mg/mL可溶性淀粉、1.50 U/mL α-淀粉酶、10.0 min反应时间，在此条件下，α-淀粉酶表现出的反应速度达到（19.53±1.74） mmol/（L·min），接近模型中的预测数值18.75 mmol/（L·min）。结论:此优化α-淀粉酶酶反应体系的方法可行，能够使α-淀粉酶在反应过程中发挥的酶活最大化，为日后在此体系下进行糖苷酶抑制剂的研究奠定了基础，具有一定的指导意义。     

The combustion behavior of epoxy-based multifunctional electrolytes

Multifunctional or structural electrolytes are characterized by ionic conductivity high enough to be used in the electrochemical devices and mechanical performance suitable for the structural applications. Preliminary insights are provided into the combustion behavior of structural bi-continuous electrolytes based on bisphenol A diglycidyl ether (DGEBA), synthesized using the techniques of reaction induced phase separation and emulsion templating. The effect of the composition of the structural electrolytes and external heat flux on the behavior of the formulations were studied using a cone calorimeter with gases formed during testing analyzed using FTIR. The composition of the formulations investigated was changed by varying the type and amount of the ion conductive part of the bi-continuous electrolyte. Two ionic liquids, 1-ethyl-3-methylimidazolium bis(fluorosulfonyl)imide (EMIM-TFSI) and 1-butyl-3-methylimidazolium tetrafluoroborate (BMIM-BF₄), as well as a deep eutectic solvent (DES) based on ethylene glycol and choline chloride, were used. The results obtained confirm that time to ignition, heat release rate (HRR), total mass loss, as well as the composition of the gases released during tests depend on the composition of the formulations. Addition of liquid electrolyte is found to reduce the time to ignition by up to 10% and the burning time by between 28% and 60% with the added benefit of reducing the HRR by at least 34%. Gaseous products such as CO₂, CO, H₂O, CH₄, C₂H₂, N₂O, NO, and HCN were detected for all formulations with the gases SO₂, NH₃, HCl, C₂H₄, and NH₃ found to be for certain formulations only.     

Application of thermophilic temperatures,low hydraulic retention times and high recycling of de-gassed effluent for higher biohydrogen production

This study investigated the external operational factors that would reduce the thermodynamic constrains preventing the simultaneous achievement of high hydrogen productivities (HPs) and hydrogen yields (HYs) in the bioreactor. At hydraulic retention time (HRT) of 1, the maximum HPs and HYs achieved was 35 L H₂/h and 3.91 mol H₂/mol glucose, respectively. At this stage, the bacterial granules occupied approximately 75% of the bioreactor and consisted of the settled biomass density of 40.6 g/L (settled granule bed height = 13.8 cm). The formation of bacterial granules improved the bioreactor performance and resulted in higher substrate conversion efficiency (95%), nutrient influent (7.5 L/h) and de-gassed effluent recycle rates (3.5 L/min). In conclusion, this study demonstrated that high nutrient influent and high de-gassed effluent recycle rates reduced the thermodynamic constrains preventing the achievement of higher H₂ productivities in the bioreactor system.     

A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings

Due to the severe outdoor PM2.5 pollution in China, many people have installed air-cleaning systems in homes. To make the systems run automatically and intelligently, we developed a recurrent neural network (RNN) that uses historical data to predict the future indoor PM2.5 concentration. The RNN architecture includes an autoencoder and a recurrent part. We used data measured in an apartment over the course of an entire year to train and test the RNN. The data include indoor/outdoor PM2.5 concentration, environmental parameters and time of day. By comparing three different input strategies, we found that a strategy employing historical PM2.5 and time of day as inputs performed best. With this strategy, the model can be applied to predict the relatively stable trend of indoor PM2.5 concentration in advance. When the input length is 2 h and the prediction horizon is 30 min, the median prediction error is 8.3 µg/m³ for the whole test set. For times with indoor PM2.5 concentrations between (20,50] µg/m³ and (50,100] µg/m³, the median prediction error is 8.3 and 9.2 µg/m³, respectively. The low prediction error between the ground-truth and predicted values shows that the RNN can predict indoor PM2.5 concentrations with satisfactory performance.     

基于时间常数的核电厂主循环泵惰转模型开发与验证

在核电厂初步设计阶段，针对反应堆进行的工况设计和安全分析均需要泵的惰转模型提供输入。现有泵的惰转模型几乎都需要已知泵的设计参数和管路系统阻力特性，但在电厂初步设计阶段，泵的详细结构设计尚未开展，管路阻力特性也难以获取。针对上述问题，提出了两种基于时间常数的主泵惰转特性曲线计算模型，并采用核电厂主循环泵的惰转试验数据进行了对比验证。分析结果表明，模型A在高转速时与试验值吻合较好，低转速时偏差较大，而模型B在整个惰转过程中与试验值均较接近，可用于核电厂的工况设计和安全分析。