Converging Technologies for Improving Human Performance: Integrating From the Nanoscale

In the early decades of the twenty-first century, concentrated efforts can unify science based on the unity of nature, thereby advancing the combination of nanotechnology, biotechnology, information technology, and new humane technologies based in cognitive science. Converging technologies integrated from the nanoscale could determine a tremendous improvement in human abilities and societal outcomes. This is a broad, cross cutting, emerging, and timely opportunity of interest to individuals, society, and humanity in the long term.About eighty scientific leaders, industry experts, and policy makers across a range of fields have contributed to develop a vision for the potential to improve human physical, mental, and social capabilities through the convergence of the four technologies. Six major themes have emerged: (a) The broad potential of converging technologies; (b) Expanding human cognition and communication; (c) Improving human health and physical capabilities; (d) Enhancing group and societal outcomes; (e) National security, and (f) Unifying science and education. This article summarizes the observations, conclusions, and recommendations made in the report (Roco & Bainbridge, eds., 2002. Converging Technologies for Improving Human Performance, NSF-DOC Report, June 2002, Arlington VA, USA).     

Complexity Versus Complex Systems: A New Approach to Scientific Discovery

Extraction of quantitative features from observations via suitable measuring devices M means that the words of science are coded as numbers, and the syntaxis is a set of mathematical rules. Once general premises are available all consequences can be worked in a purely deductive way. This characteristic of science displays two orders of drawbacks, namely, undecidability of deductive procedures, and intractability of computer modelings of complex situations. The way out of such a crisis consists in an adaptive strategy, that is, in a frequent readjustment of M suggested by the observed events. As a consequence, M provides different data streams (words) for the same observed events, as it is tuned to different resolutions. The adaptive strategy here introduced should by no means be confused with the adaptivity of a learning machine, which—inputted by a data stream—readjusts itself over a class of theoretical explanations in order to select the optimal one, thus providing knowledge conditional on the assigned input. On the contrary, physics aims at extracting regular patterns out of things, by a trial and error procedure which includes not only modifications of the explanations for fixed data sets, but also exploring different data sets via modified M's. This M-adjustment is a pre-linguistic endeavour, not expressible by a formal language. Such an essential characteristic of the physical program means that physics can not be performed by a machine.     

Human cognitive paradigm and its application in semi-supervised learning

In many practical data mining applications such as web page classification, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning algorithms such as Tri-training have attracted much attention. However, mislabeling the unlabeled data during the learning process is an inevitable problem and harms the performance improvement of the hypothesis. To solve this problem, a novel human cognitive paradigm is constructed for semi-supervised learning in this paper. In detail, based on local distribution of feature space, the majority voting scheme is substituted by an estimation of the probability of sample to belong to a certain class as an efficient strategy for data editing. It considers the form of the underlying probability distribution in the neighborhood of a point to identify and remove the mislabeled data. Validation of the proposed method is performed with extensive experiments. Results demonstrate that compared with Tri-training method, our method can more effectively and stably exploit unlabeled data to enhance the learning performance.     

Evolutionary learning of fuzzy grey cognitive maps for the forecasting of multivariate,interval-valued time series

Time series are built as a result of real-valued observations ordered in time; however, in some cases, the values of the observed variables change significantly, and those changes do not produce useful information. Therefore, within defined periods of time, only those bounds in which the variables change are considered. The temporal sequence of vectors with the interval-valued elements is called a ‘multivariate interval-valued time series.’ In this paper, the problem of forecasting such data is addressed. It is proposed to use fuzzy grey cognitive maps (FGCMs) as a nonlinear predictive model. Using interval arithmetic, an evolutionary algorithm for learning FGCMs is developed, and it is shown how the new algorithm can be applied to learn FGCMs on the basis of historical time series data. Experiments with real meteorological data provided evidence that, for properly-adjusted learning and prediction horizons, the proposed approach can be used effectively to the forecasting of multivariate, interval-valued time series. The domain-specific interpretability of the FGCM-based model that was obtained also is confirmed.     

A Comparison of TIMSS Items Using Cognitive Domains

The results of international assessments such as the Trends in International Mathematics and Science Study (TIMSS) are often reported as rankings of nations. Focusing solely on national rank can result in invalid inferences about the relative quality of educational systems that can, in turn, lead to negative consequences for teachers and students. This study seeks an alternative data analysis method that allows for improved inferences about international performance on the TIMSS. In this study, four classroom teachers categorized a sample of TIMSS items by the cognitive domains of knowing and applying using the definitions provided by the TIMSS 2011 Assessment Frameworks. Items of different cognitive domains were analyzed separately. This disaggregation allowed for more valid inferences to be made about student performance. Results showed almost no significant difference between the performance of U.S. students and the students of five other nations. Additionally, no differences were observed in U.S. students' performance on knowing items and applying items, although students from some sample nations performed significantly better on knowing items. These results suggest that policy makers, educators, and citizens should be cautious when interpreting the results of TIMSS rank tables.     

Inhibition of Insulin-Regulated Aminopeptidase (IRAP) by Arylsulfonamides

The inhibition of insulin-regulated aminopeptidase (IRAP, EC 3.4.11.3) by angiotenesin IV is known to improve memory and learning in rats. Screening 10 500 low-molecular-weight compounds in an enzyme inhibition assay with IRAP from Chinese Hamster Ovary (CHO) cells provided an arylsulfonamide (N-(3-(1H-tetrazol-5-yl)phenyl)-4-bromo-5-chlorothiophene-2-sulfonamide), comprising a tetrazole in the meta position of the aromatic ring, as a hit. Analogues of this hit were synthesized, and their inhibitory capacities were determined. A small structure–activity relationship study revealed that the sulfonamide function and the tetrazole ring are crucial for IRAP inhibition. The inhibitors exhibited a moderate inhibitory potency with an IC₅₀=1.1±0.5 μm for the best inhibitor in the series. Further optimization of this new class of IRAP inhibitors is required to make them attractive as research tools and as potential cognitive enhancers.     

Characterizing Interaction with Visual Mathematical Representations

This paper presents a characterization of computer-based interactions by which learners can explore and investigate visual mathematical representations (VMRs). VMRs (e.g., geometric structures, graphs, and diagrams) refer to graphical representations that visually encode properties and relationships of mathematical structures and concepts. Currently, most mathematical tools provide methods by which a learner can interact with these representations. Interaction, in such cases, mediates between the VMR and the thinking, reasoning, and intentions of the learner, and is often intended to support the cognitive tasks that the learner may want to perform on or with the representation. This paper brings together a diverse set of interaction techniques and categorizes and describes them according to their common characteristics, goals, intended benefits, and features. In this way, this paper aims to provide a preliminary framework to help designers of mathematical cognitive tools in their selection and analysis of different interaction techniques as well as to foster the design of more innovative interactive mathematical tools. An effort is made to demonstrate how the different interaction techniques developed in the context of other disciplines (e.g., information visualization) can support a diverse set of mathematical tasks and activities involving VMRs.     

Dynamic systems view of learning a three‐tiered theory in physics: robust learning outcomes as attractors

The process of learning scientific knowledge from the dynamic systems viewpoint is studied in terms probabilistic learning model (PLM), where learning accrues from foraging in the epistemic landscape. The PLM leads to the formation of attractor‐type regions of preferred models in an epistemic landscape. The attractor‐type states correspond to robust learning outcomes which are more probable than others. These can be assigned either to the high confidence in model selection or to the dynamic evolution of a learner's proficiency, which depends on the learning history. The results suggest that robust learning states are essentially context dependent, and that learning is a continuous development between these context dependent states. © 2016 Wiley Periodicals, Inc. Complexity 21: 259–267, 2016     

Simultaneous detection of fluquinconazole and flusilazole in lettuce using gas chromatography with a nitrogen phosphorus detector: decline patterns at two different locations

Method validations in addition to decline patterns of fluquinconazole and flusilazole in lettuce grown under greenhouse conditions at two different locations were investigated. Following the application of fluquinconazole and flusilazole at a dose rate of 20 mL/20 L water, lettuce samples were collected randomly for up to 7 days post‐application, and simultaneously extracted with acetone, purified through solid‐phase extraction, analyzed via gas chromatography with a nitrogen phosphorus detector, and confirmed through gas chromatography–mass spectrometry. The linearity was excellent, with determination coefficients (R²) between 0.9999 and 1.0. The method was validated in triplicate at two different spiking levels (0.2 and 1.0 mg/kg) with satisfactory recoveries between 75.7 and 97.9% and relative standard deviations of <9. The limit of quantification was 0.01 mg/kg. Both analytes declined very quickly, as can be seen from the short half‐life time of <4 days. Statistical analysis revealed significant differences between residues at different days of sampling, except at 7 days post‐application (triple application). At that point, the decline patterns of fluquinconazole and flusilazole were independent of application rate, location, temperature and humidity. Copyright © 2015 John Wiley & Sons, Ltd.     

基于概念认识功能的教学案例*—以“物质的量”为例

分析了“物质的量”概念教学存在的2个问题:概念结构不清晰,忽视概念之间关系的建立,概念的呈现不能从原有认知出发;忽视概念的认识功能,将物质的量仅仅当成计算的工具。提出在教学中从初三原有化学定量出发,从朴素的个数定量和宏观的质量定量发展到科学的化学计量—物质的量,借助板书建立了物质的量从内涵到关系的概念结构图。呈现了突出物质的量的认识功能、促进化学计量认识发展的教学实录,包括概念建立、概念理解和概念应用3个环节,并通过访谈分析了教学效果和存在问题及改进方向。