Conditional Dynamic Mutual Information-Based Feature Selection

With emergence of new techniques, data in many fields are getting larger and larger, especially in dimensionality aspect. The high dimensionality of data may pose great challenges to traditional learning algorithms. In fact, many of features in large volume of data are redundant and noisy. Their presence not only degrades the performance of learning algorithms, but also confuses end-users in the post-analysis process. Thus, it is necessary to eliminate irrelevant features from data before being fed into learning algorithms. Currently, many endeavors have been attempted in this field and many outstanding feature selection methods have been developed. Among different evaluation criteria, mutual information has also been widely used in feature selection because of its good capability of quantifying uncertainty of features in classification tasks. However, the mutual information estimated on the whole dataset cannot exactly represent the correlation between features. To cope with this issue, in this paper we firstly re-estimate mutual information on identified instances dynamically, and then introduce a new feature selection method based on conditional mutual information. Performance evaluations on sixteen UCI datasets show that our proposed method achieves comparable performance to other well-established feature selection algorithms in most cases

The Importance of Fracture Geometry and Matrix Data on Transient Hydraulic Tomography in Fractured Rocks: Analyses of Synthetic and Laboratory Rock Block Experiments

The accurate characterization of hydraulic properties within fractured geologic media as well as the imaging of fracture patterns and their connectivity have been difficult to accomplish over the last few decades. Recently, hydraulic tomography has been suggested as a promising approach for imaging the key hydraulic parameters such as hydraulic conductivity (K) and specific storage (Ss) distribution of fractured geologic media. This thesis investigates the importance of geologic information on HT analyses through synthetic experiments and laboratory rock block experiments conducted by Sharmeen et al. (2012). Specifically, three inverse modeling approaches with different types of geologic data included in the analyses were examined: 1) homogeneous estimates of hydraulic parameters without geologic data; 2) correct fracture locations and matrix data with correct hydraulic parameters; and 3) partially correct fracture locations and matrix data with incorrect hydraulic parameters. In this study, the assessment of transient hydraulic tomography (THT) is conducted in fractured dolomitic rock block through the Sequential Successive Linear Estimator (SSLE) developed by Zhu and Yeh (2005). The image of fracture patterns and their connectivity are presented through maps of K and Ss distributions (or tomograms). The validation of inverse modeling results is quantitatively performed through the prediction of independently conducted pumping tests not used in the calibration effort. The comparison among results obtained from different approaches indicates that: 1) THT analysis can capture the overall fracture pattern and their hydraulic properties (K and Ss), but the estimated values are higher where observations are limited; 2) using a correct geological model as prior information in a geostatistical inverse model can preserve geologic features especially within the matrix, where drawdown data are hard to obtain; 3) a simple model without any geologic information is more reliable than the one based on the wrong description of geologic features. Overall, the results from this study indicate the importance of incorporating accurate geological data in HT surveys when drawdown data are sparse and not available within the matrix, which could have critical implications for field research in fractured rock hydrogeology

The study on the impact of short video tourism Vloggers at social media platform on online sharing intention

COVID-19 has caused significant damage globally, including tourism. This study adopts the quantitative research method, selects 588 samples from tourists watching short videos to investigate the antecedents and effects of parasocial interaction between tourists and short video tourism Vloggers, and analyses them with partial least squares. Based on parasocial relationship theory, this study investigates the antecedents of parasocial relationships between tourists and short video tourism Vloggers and their willingness to share short video tourism. Results show that the consistency of values, entertainment motivation, and emotional engagement positively impact the parasocial relationships between tourists and short video tourism Vloggers and affect the online sharing intention through the parasocial relationship. The consistency of values can directly affect sharing intention. As an intermediary variable, parasocial relationship positively impacts value congruence, entertainment motivation, emotional engagement, and sharing intention. This study introduces parasocial relationship into the research of tourism short video Vloggers, which enriches the literature. Furthermore, this introduction provides new marketing strategies and suggestions for the sustainable development of tourism

Cloning and characterization of maize ZmSPK1, a homologue to nonfermenting1-related protein kinase2

SnRK2s play important roles in plant stresses responses. One full-length cDNA encoding a SnRK2b homologue was isolated from maize by RT-PCR and named as ZmSPK1 (for stress-induced protein kinase). The ZmSPK1 protein has 364 amino acids with an estimated molecular mass of 41.8 KD and an isoelectric point of 5.8. The deduced protein sequence has the closest identities to the members of SnRK2b group. RT-PCR analysis showed that the ZmSPK1 expression was induced by mannitol, salt and abscisic acid (ABA). Furthermore, in different tissues the ZmSPK1 showed different expression patterns and was most abundant in reproductive organs. These results suggested that ZmSPK1 might play multiple roles in abiotic stress resistance pathways, as well as in plant reproductive development.Key words: Zea mays L., SnRK2b, expression pattern, abiotic stres

Innovative and Responsible Governance of Nanotechnology for Societal Development

The one-pot synthesis of iron-doped carbon quantum dots (Fe-CQDs) for use as both magnetic resonance (MR) and fluorescent (dual-mode) imaging nanoprobes is described. Comprehensive characterizations of the material confirmed the successful doping of the CQDs with Fe(II) ions. The imaging probe has a longitudinal relaxivity of 3.92 mM−1∙s−1 and a low r2/r1 ratio of 1.27, both of which are critical for T1-weighted contrast agents. The maximum emission of Fe-CQDs locates at 450 nm under 375 nm excitation, which also can be applied to fluorescence imaging. Biotoxicity assessment showed good biocompatibility of the Fe-CQDs. The in-vitro experiments with A549 cells indicated that the Fe-CQDs are viable candidates as dual-mode (MR/fluorescence) imaging nanoprobes. For in-vivo experiments, they exhibit high contrast efficiency, thereby improving the positive contrast in T1-weighted MR images. In-vivo time-dependent MRI of major organs showed that the Fe-CQDs undergo fast glomerular filtration and can evade immuno-absorption due to their ultra-small size and excellent biocompatibility. [Figure not available: see fulltext.]

An Ensemble Method for High-Dimensional Multilabel Data

Multilabel learning is now receiving an increasing attention from a variety of domains and many learning algorithms have been witnessed. Similarly, the multilabel learning may also suffer from the problems of high dimensionality, and little attention has been paid to this issue. In this paper, we propose a new ensemble learning algorithms for multilabel data. The main characteristic of our method is that it exploits the features with local discriminative capabilities for each label to serve the purpose of classification. Specifically, for each label, the discriminative capabilities of features on positive and negative data are estimated, and then the top features with the highest capabilities are obtained. Finally, a binary classifier for each label is constructed on the top features. Experimental results on the benchmark data sets show that the proposed method outperforms four popular and previously published multilabel learning algorithms

Metal-Free Photoredox Intramolecular Cyclization of N-Aryl Acrylamides

A novel metal-free photoredox-catalyzed cyclization reaction of N-aryl acrylamide is herein reported that provides synthetically valuable oxindole derivatives through the bis-mediation of H2O and aldehyde. In this work, sustainable visible light was used as the energy source, and the organic light-emitting molecule 4CzIPN served as the efficient photocatalyst. The main characteristics of this reaction are environmentally friendly and high yields

Progress in C-C and C-Heteroatom Bonds Construction Using Alcohols as Acyl Precursors

Acyl moiety is a common structural unit in organic molecules, thus acylation methods have been widely explored to construct various functional compounds. While the traditional Friedel–Crafts acylation processes work to allow viable construction of arylketones under harsh acid conditions, recent progress on developing acylation methods focused on the new reactivity discovery by exploiting versatile and easily accessible acylating reagents. Of them, alcohols are cheap, have low toxicity, and are naturally abundant feedstocks; thus, they were recently used as ideal acyl precursors in molecule synthesis for ketones, esters, amides, etc. In this review, we display and discuss recent advances in employing alcohols as unusual acyl sources to form C-C and C-heteroatom bonds, with emphasis on the substrate scope, limitations, and mechanism