A Dual Mechanism of Cognition and Emotion in Processing Moral-Vertical Metaphors

A moral concept involves two main factors: moral cognition (indicated by morality) and emotion (indicated by emotionality). The cognitive mechanism underlying moral metaphors on the vertical dimension (e.g., moral-up, immoral-down) was investigated in three experiments using implicit association tests. The results of Experiment 1 show a stronger association of “moral-up, immoral-down” between words high in morality and vertical space than between words low in morality and vertical space, which indicates that cognitive factors of morality facilitate the processing of vertical spatial metaphors of moral concepts. Experiment 2, employing moral words different in emotionality, reveals a stronger association of “moral-up, immoral-down” between words high in emotionality and vertical space than between words low in emotionality and vertical space, which shows that emotional factors of morality facilitate the processing of vertical spatial metaphors of moral concepts. A comparison between the two experiments suggests a faster response to emotion than to moral cognition and similar association strengths of the two factors with verticality. Using words high in morality and emotionality, Experiment 3 shows that a combination of the two conditions (i.e., high morality and high emotionality) leads to a stronger tie with verticality than either condition. The above three experiments indicate that both moral cognition and emotion facilitate the processing of vertical spatial metaphor of moral concepts, and the forces of the two, which jointly affect the metaphorical connection between morality and verticality, are basically equal, although the processing of emotionality is faster than that of morality

Land Cover Classification Using Integrated Spectral, Temporal, and Spatial Features Derived from Remotely Sensed Images

Obtaining accurate and timely land cover information is an important topic in many remote sensing applications. Using satellite image time series data should achieve high-accuracy land cover classification. However, most satellite image time-series classification methods do not fully exploit the available data for mining the effective features to identify different land cover types. Therefore, a classification method that can take full advantage of the rich information provided by time-series data to improve the accuracy of land cover classification is needed. In this paper, a novel method for time-series land cover classification using spectral, temporal, and spatial information at an annual scale was introduced. Based on all the available data from time-series remote sensing images, a refined nonlinear dimensionality reduction method was used to extract the spectral and temporal features, and a modified graph segmentation method was used to extract the spatial features. The proposed classification method was applied in three study areas with land cover complexity, including Illinois, South Dakota, and Texas. All the Landsat time series data in 2014 were used, and different study areas have different amounts of invalid data. A series of comparative experiments were conducted on the annual time-series images using training data generated from Cropland Data Layer. The results demonstrated higher overall and per-class classification accuracies and kappa index values using the proposed spectral-temporal-spatial method compared to spectral-temporal classification methods. We also discuss the implications of this study and possibilities for future applications and developments of the method

Anti-reflection for monocrystalline silicon from diamond-like carbon films deposited by magnetron sputtering

In this work, diamond-like carbon (DLC) films used as anti-reflective coatings for monocrystalline silicon were deposited by magnetron sputtering for potential application in solar cells. The microstructural and optical properties of the films were investigated as a function of substrate temperature over a wide range during deposition. It showed that, when the substrate temperature increased from RT to 800 °C, the hybridized structures of the DLC films accordingly changed associated with a significant variation of refractive index between2.22 and 1.64 at a wavelength of 550 nm. Three types of coating systems, namely single-, three- and five-layer films on monocrystalline silicon substrates, were designed based on the anti-reflection principle and fabricated in terms of the relationships of refractive index and deposition rate with substrate temperature. In particular, a well-designed three-layer film, of which the refractive index gradually changed along the thickness, that is 1.8, 1.9 and 2.0, respectively, was successfully deposited at one step on monocrystalline silicon substrates by adjusting substrate temperature and deposition time, and featured a broadband anti-reflective characteristic with low average reflectivity of 8.7% at a wide solar spectrum of 400–1100 nm. This work demonstrates that the DLC film has a promising application potential as broadband anti-reflective coatings in silicon-based solar cells

Web of Things-Based Remote Monitoring System for Coal Mine Safety Using Wireless Sensor Network

Frequent accidents have occurred in coal mine enterprises; therefore, raising the technological level of coal mine safety monitoring systems is an urgent problem. Wireless sensor networks (WSN), as a new field of research, have broad application prospects. This paper proposes a Web of Things- (WoT-) based remote monitoring system that takes full advantage of wireless sensor networks in combination with the CAN bus communication technique that abstracts the underground sensor data and capabilities into WoT resources to offer services using representational state transfer (REST) style. We also present three different implemented scenarios for WoT-based remote monitoring systems for coal mine safety, for which the system performance has been measured and analyzed. Finally, we describe our conclusions and future work

Covalent modification of temperature-sensitive breathable polyurethane with carbon nanotubes

<p>Intelligent breathable polyurethane (PU) that is easily allowable for vapor transmission at critical temperature would have significant implication for numerous applications; however, fabrication of such materials has proven to be tremendously challenging. Herein, we reported novel breathable polyurethane material covalently modified with carbon nanotubes (CNTs). When an optimal amount of CNTs (0.5 wt%) was added, the resultant PU film presented high waterproofness with hydrostatic pressure up to 10.9 kPa, as well as enhanced mechanical properties with a tensile strength of 22.2 kPa and elongation at break of 990%. This smart PU film has a significant increase in water vapor transmission rate between 18°C (1400 g/(m²·d)) and 38°C (3440 g/(m²·d)). The type of intelligent polyurethane material is a promising candidate for applications in areas such as protective clothing, separator media, and wearable electronics.</p