EmotionMap: Visual Analysis of Video Emotional Content on a Map

Journal of Computer Science and Technology - Emotion plays a crucial role in gratifying users’ needs during their experience of movies and TV series, and may be underutilized as a framework...     

Selective hydrogenation of acetylene over Pd/CeO2

Five hundred ppm Pd/CeO₂ catalyst was prepared and evaluated in selective hydrogenation of acetylene in large excess of ethylene since ceria has been recently found to be a reasonable stand-alone catalyst for this reaction. Pd/CeO₂ catalyst could be activated in situ by the feed gas during reactions and the catalyst without reduction showed much better ethylene selectivity than the reduced one in the high temperature range due to the formation of oxygen vacancies by reduction. Excellent ethylene selectivity of ~100% was obtained in the whole reaction temperature range of 50°C–200°C for samples calcined at temperatures of 600°C and 800°C. This could be ascribed to the formation of Pd_xCe_1−xO_2−y or Pd-O-Ce surface species based on the X-ray diffraction and X-ray photoelectron spectroscopy results, indicating the strong interaction between palladium and ceria.     

Immobilization of surface non-affinitive protein onto a metal surface by an external electric field

1. Download : Download high-res image (279KB)
2. Download : Download full-size image

     

基于GFU和分层LSTM的组群行为识别研究方法

提出一种以"关键人物"为核心，使用门控融合单元（GFU，Gated Fusion Unit）进行特征融合的组群行为识别框架，旨在解决两个问题：①组群行为信息冗余，重点关注关键人物行为特征，忽略无关人员对组群行为的影响；②组群内部交互行为复杂，使用GFU有效融合以关键人物为核心的交互特征，再通过LSTM时序建模成为表征能力更强的组群特征.最终，通过softmax分类器进行组群行为类别分类.该算法在排球数据集上取得了86.7%的平均识别率.     

Double Mutant Cycles as a Tool to Address Folding,Binding, and Allostery

Quantitative measurement of intramolecular and intermolecular interactions in protein structure is an elusive task, not easy to address experimentally. The phenomenon denoted ‘energetic coupling’ describes short- and long-range interactions between two residues in a protein system. A powerful method to identify and quantitatively characterize long-range interactions and allosteric networks in proteins or protein–ligand complexes is called double-mutant cycles analysis. In this review we describe the thermodynamic principles and basic equations that underlie the double mutant cycle methodology, its fields of application and latest employments, and caveats and pitfalls that the experimentalists must consider. In particular, we show how double mutant cycles can be a powerful tool to investigate allosteric mechanisms in protein binding reactions as well as elusive states in protein folding pathways.     

A toy or a friend? Children's anthropomorphic beliefs about robots and how these relate to second-language word learning

This study investigates the degree to which children anthropomorphize a robot tutor and whether this anthropomorphism relates to their vocabulary learning in a second-language (L2) tutoring intervention. With this aim, an anthropomorphism questionnaire was administered to 5-year-old children (N = 104) twice: prior to and following a seven-session L2 vocabulary training with a humanoid robot. On average, children tended to anthropomorphize the robot prior to and after the lessons to a similar degree, but many children changed their attributed anthropomorphic features. Boys anthropomorphized the robot less after the lessons than girls. Moreover, there was a weak but significant positive correlation between anthropomorphism as measured before the lessons and scores on a word-knowledge post-test administered the day after the last lesson. There was also a weak but significant positive correlation between the change in anthropomorphism over time and scores on a word-knowledge post-test administered approximately 2 weeks after the last lesson. Our results underscore the need to manage children's expectations in robot-assisted education. Also, future research could explore adaptations to individual children's expectations in child-robot interactions.     

Hydrogen adsorption of O/N-rich hierarchical carbon scaffold decorated with Ni nanoparticles: Experimental and computational studies

Hierarchical carbon scaffold (HCS) with multi-porous structures, favoring hydrogen diffusion and physisorption is doped with 2–10 wt % Ni for storing hydrogen at ambient temperature. Due to N- and O-rich structure of melamine-formaldehyde resin used as carbon precursor, homogeneous distribution of heteroatoms (N and O) in HCS is achieved. 2 wt % Ni-doped HCS shows the highest hydrogen capacity up to 2.40 wt % H₂ (T = 298 K and p (H₂) = 100 bar) as well as excellent reversibility of 18.25 g H₂/L and 1.25 wt % H₂ (T = 298 K and p (H₂) = 50 bar) and electrical production from PEMFC stack up to 0.7 Wh upon eight cycles. Computations and experiments confirm strong interactions between Ni and heteroatoms, leading to uniform distribution small particles of Ni. This results in enhancing reactive surface area for hydrogen adsorption and preventing agglomeration of Ni nanoparticles upon cycling. Ni K-edge XANES spectra simulated from the optimized structure of Ni-doped N/O-rich carbon using DFT calculations are consistent with the experimental spectra and suggest electron transfer from Ni to hydrogen to form Ni–H bond upon adsorption. Considering low desorption temperature (323 K), not only chemisorbed hydrogen is involved in adsorption mechanisms but also physisorption and spillover of hydrogen.     

Effects of breed,farm intensiveness,and cow productivity level on cheese-making ability predicted using infrared spectral data at the population level

Fourier-transform infrared (FTIR) spectra collected during milk recording schemes at population level can be used for predicting novel traits of interest for farm management, cows' genetic improvement, and milk payment systems. The aims of this study were as follows. (1) To predict cheese yield traits using FTIR spectra from routine milk recordings exploiting previously developed calibration equations. (2) To compare the predicted cheese-making abilities of different dairy and dual-purpose breeds. (3) To analyze the effects of herds' level of intensiveness (HL) and of the cow's level of productivity (CL). (4) To compare the patterns of predicted cheese yields with the patterns of milk composition in different breeds to discern the drivers of cheese-making efficiency. The major sources of variation of FTIR predictions of cheese yield ability (fresh cheese or cheese solids produced per unit milk) of individual milk samples were studied on 115,819 cows of 4 breeds (2 specialized dairy breeds, Holstein and Brown Swiss, and 2 dual-purpose breeds, Simmental and Alpine Grey) from 6,430 herds and exploiting 1,759,706 FTIR test-day spectra collected over 7 yr of milk sampling. Calibration equations used were previously developed on 1,264 individual laboratory model cheese procedures (cross-validation R² 0.85 and 0.95 for fresh and solids cheese yields, respectively). The linear model used for statistical analysis included the effects of parity, lactation stage, year of calving, month of sampling, HL, CL, breed of cow, and the interactions breed × HL and breed × CL. The HL and CL stratifications (5 classes each) were based on average daily secretion of milk net energy per cow. All effects were highly significant. The major conclusions were as follows. (1) The FTIR-based prediction of cheese yield of milk goes beyond the knowledge of fat and protein content, partially explaining differences in cheese-making ability in different cows, breeds and herds. (2) Differences in cheese yields of different breeds are only partially explained by milk fat and protein composition, and less productive breeds are characterized by a higher milk nutrient content as well as a higher recovery of nutrients in the cheese. (3) High-intensive herds not only produce much more milk, but the milk has a higher nutrient content and a higher cheese yield, whereas within herds, compared with less productive cows, the more productive cows have a much greater milk yield, milk with a greater content of fat but not of protein, and a moderate improvement in cheese yield, differing little from expectations based on milk composition. Finally, (4) the effects of HL and CL on milk quality and cheese-making ability are similar but not identical in different breeds, the less productive ones having some advantage in terms of cheese-making ability. We can obtain FTIR-based prediction of cheese yield from individual milk samples retrospectively at population level, which seems to go beyond the simple knowledge of milk composition, incorporating information on nutrient retention ability in cheese, with possible advantages for management of farms, genetic improvement of dairy cows, and milk payment systems.     

“电力系统分析”课程可持续发展的建设与实践

本文探索了新工科背景下“电力系统分析”课程建设情况,摸索工程教育的新理念,以学生发展为中心,尝试翻转课堂的互动式教学模式,实现了创新人才的培养目标。编著《电力系统分析》教材,实现了内容的继承和创新、重要知识点的交叉和融合。建设数字仿真及物理模拟实验室,解决高校人才培养与社会需求脱节的问题。以“注重学习过程,加强平时考核,淡化期末考试”的方式进行综合考评,提高了教学效果与教学质量。经过几年的教学探索与实践,证明了本课程建设效果良好。     

Insect-corn kernel interaction: Chemical signaling of the grain and host recognition by Sitophilus zeamais

In living organisms, the cuticle has structural functions and is involved through chemical signaling in biological interactions such as plant-insect and provides protection against biotic and abiotic factors, thereby avoiding desiccation or the attack of predators. The objective of this study was to investigate the participation of the epicuticle in the maize kernel-Sitophilus zeamais interaction. The GC-MS analysis of the epicuticle extract demonstrated the presence of aliphatic hydrocarbons, alcohols, ethers, fatty acids, sterols and their derivatives. The results of bioassays show that the epicuticle of maize has a primordial role in its interaction with S. zeamais, and participates in the recognition and attraction to the food source, as well as regulating its reproduction. In addition the compounds present in the epicuticle extract may act as signal molecules and development regulators. This study reveals the effect of the maize kernel cuticle on Sitophilus behavior and contributes to the understanding of the interaction.