Determinants of the intention to use non-medical insertable digital devices: The case of Chile and Colombia

In the past, thinking of carrying electronic devices inside our bodies was only posed by non-real scenarios. The emergence of insertable devices has changed this. Since this technology is still in its initial development stages, few studies have investigated factors that influence its acceptance. This paper analyzes the predictors of the intention to use non-medical insertable devices in two Latin American contexts. We used partial least squares structural equation modeling to examine whether six constructs predicted intention to use insertable devices. A questionnaire was administered to undergraduate students located in Colombia and Chile (n = 672). We also examined whether these predictors influenced intention differently for both of them. Four common constructs significantly and positively influenced both Chilean and Colombian respondents to use insertable devices (hedonic motivation, habit, performance expectancy, and social influence). Also, the habit has a complementary mediating effect on the relationship between social influence and behavioral intention. By contrast, effort expectations were a positive and significant predictor, but only among Chilean respondents. Findings suggest that when technologies are emerging, well-known predictors of intention (e.g., performance and effort expectations) are less influential than predictors related to self-efficacy (e.g., habit and hedonic motivation). The use of insertable devices has a significant impact on society. Thus, a better understanding of what motivates their use has implications for both academia and industry.     

Combined hardware–software multi-parallel prefiltering on the Convey HC-1 for fast homology detection

Protein databases used in research are huge and still grow at a fast pace. Many comparisons need to be done when searching similar (homologous) sequences for a given query sequence in these databases. Comparing a query sequence against all sequences of a huge database using the well-known Smith–Waterman algorithm is very time-consuming. Hidden Markov Models pose an opportunity for reducing the number of entries of a database and also enable to find distantly homologous sequences. Fewer entries are achieved by clustering similar sequences in a Hidden Markov Model. Such an approach is used by the bioinformatics tool HHblits. To further reduce the runtime, HHblits uses two-level prefiltering to reduce the number of time-consuming Viterbi comparisons. Still, prefiltering is very time-consuming. Highly parallel architectures and huge bandwidth are required for processing and transferring the massive amounts of data. In this article, we present an approach exploiting the reconfigurable, hybrid computer architecture Convey HC-1 for migrating the most time-consuming part. The Convey HC-1 with four FPGAs and high memory bandwidth of up to 76.8 GB/s serves as the platform of choice. Other bioinformatics applications have already been successfully supported by the HC-1. Limited by FPGA size only, we present a design that calculates four first-level prefiltering scores per FPGA concurrently, i.e. 16 calculations in total. This score calculation for the query profile against database sequences is done by a modified Smith–Waterman scheme that is internally parallelized 128 times in contrast to the original Streaming ‘Single Instruction Multiple Data (SIMD)’ Extensions (SSE)-supported implementation where only 16-fold parallelism can be exploited and where memory bandwidth poses the limiting factor. Preloading the query profile, we are able to transform the memory-bound implementation to a compute- and resource-bound FPGA design. We tightly integrated the FPGA-based coprocessor into the hybrid computing system by employing task-parallelism for the two-level prefiltering. Despite much lower clock rates, the FPGAs outperform SSE-based execution for the calculation of the prefiltering scores by a factor of 7.9.     

深层及非生物成烃的催化机制

讨论了非生物成因烃的可能生成机制。通过对比分析,探讨了在地质条件下碳、氢经历费-托合成和由过渡金属催化产生烃的可能机制。     

Model-based detection of critical driving situations with fuzzy logic decision making

A precise knowledge about the current driving condition is getting increasingly important for future driver assistance systems like global chassis control or collision avoidance systems for avoiding any critical driving situation. Moreover a precise knowledge about the driving situation can be used in testing, in evaluation, and for comparison of new passenger cars. A two degree of freedom model of vehicle lateral dynamics is used to derive a characteristic velocity stability indicator (CVSI). The CVSI is used to distinguish between different driving and stability conditions (i.e. understeering, oversteering, and neutralsteering). This forms the basis for a driving condition detection system with fixed thresholds. It is then extended to a detection system with fuzzy logic thresholds. The CVSI and the fuzzy systems are compared experimentally using (i) a slalom test drive on an icy road and (ii) a stationary circular test drive on a dry asphalt road.     

岩石圈深部温度压力条件下褐煤与水的热模拟研究

在压力高达1—3GPa、温度为400—700℃的条件下，在密闭体系中进行了褐煤加水的模拟实验。分析了实验产物中液态烃的变化规律，并讨论了压力、温度及恒温时间对有机质演化的影响。实验结果表明，热模拟液态产物氯仿沥青“A”的有机碳含量为0．91％-2．55％，2GPa条件下其高峰值后移至700℃，说明高压抑制了液态烃的生成，同时压力升高有利于有机质降解产物的环化、聚合和芳构化。在400—600℃条件下，温度升高或恒温时间增长，OEP和Pr／Ph值均减小；而在700℃的恒温条件下，压力增高，OEP和Pr／Ph值均增大。说明有机质的成熟度与温度和加热时间成正相关，而压力增加抑制了有机质的成熟演化。在高压条件下，芳烃演化的主要趋势是甲基化作用，压力升高有利于甲基化反应和甲基重排。     

Improved resolution of the multiple inverse method by eliminating erroneous solutions

The multiple inverse method is a numerical technique designed to separate stresses from heterogeneous fault–slip data. The method is one of the resampling methods based on the pattern recognition. Plotting solutions determined from k-fault subset into the parameter space, we have clusters representing significant stresses for the dataset. This paper presents a technique to improve the resolution of stress for the method. The regularity/singularity test of the subsets taken from the fault–slip data is the key for this purpose. The resolution and accuracy of the method are improved by eliminating erroneous stresses or artifacts that were yielded by the method. The performances of the method are demonstrated with a variety of artificial datasets.     

建立速度模型的层析成像方法研究

针对目前叠前深度偏移速度模型建立中，全局反演的层析成像方法存在稳定性和走时拾取困难，以及局部反演的偏移速度分析大多存在假设条件过于苛刻等问题，通过分析，提出了利用地震同相辅的多种信息进行层析反演，并在反演中加入富有地质含义的倾角信息以增加反演过程的稳定性和收敛速度，利用类似于叠加速度谱的斜率速度谱来拾取地震同相轴的斜率，抛开了层位反演要求拾取同一同相轴这一实际应用难度很大的条件，使拾取方便，算法稳定。通过理论试算，证明了本方法的有效性。     

运移路径模拟和应用尝试

油气二次运移的方式和方向对具体指出盆地油气资源量所赋存的位置有决定性的意义.在运载层中推动油气运动的作用力主要是浮力和毛细管力,而盖层则起着毛细管阻力作用.因此盖层的形状及空间分布就成了影响运移路径重要的封盖因素.该文试图根据盖层的几何形状特性,建立运移路径模拟模型,开发微机软件运行实施.在一个具体盆地进行运移路径模拟应用的结果反映,与已知勘探情况基本符合,能够检验油气期次性运移的地质概念,可以揭示运移过程,预测可能的运移聚集位置.      

A Diffusion-Compensation Relation for Normal Grain Growth and Grain-Boundary Diffusion of Oxygen in Oxides

Values of D₀/δ and Q for grain-boundary diffusion, derived from published studies of normal grain growth in Al₂O₃, BeO, CaO, MgO, SiO₂, and CaSiO₃, are fit by the linear compensation equation log D₀/δ=0.03170Q -7.6792 (r²=0.9384). Comparison of grain-boundary diffusion coefficients derived from grain growth in oxides with those obtained by direct experimental measurement suggests that the kinetics of normal grain growth are controlled by grain-boundary diffusion of oxygen.     

ModDRE: A program to model deepwater-reservoir elements using geomorphic and stratigraphic constraints

In deepwater-reservoir modeling, the proper representation of the spatial distribution of architectural elements is important to account for pore-volume distribution and the connectivity of reservoir sand bodies. This is especially critical for rock and fluid-volume estimates, reservoir-performance predictions, and development-well planning.A new integrated stochastic reservoir-modeling approach (ModDRE—Modeling Deepwater Reservoir Elements) accounts for geomorphic and stratigraphic controls to generate the deepwater-reservoir architecture. Information on stratal-package evolution and sediment provenance can be integrated into the reservoir-modeling process. A slope-area analytical approach is implemented to account for topographical constraints on channel and sheet-form reservoir architectures and their distribution. Inferred sediment–source statistics and architectural-element variability (from seismic, outcrop, and stratigraphic studies) associated with relative changes in sea level can also be used to constrain the deepwater-reservoir-element statistics. Based on these geomorphic and stratigraphic constraints, deepwater-reservoir elements (channels, lobes) are built into the model sequentially (in stratigraphic order).Integration of realistic geological and engineering attributes into deepwater-reservoir models is vital for optimal reservoir management. This approach is unique in that it is more directly constrained to geomorphic and stratigraphic parameters than traditional object- or surface-based techniques for stochastic deepwater-reservoir modeling.