Computational Intelligence for the Micro Learning

The developments of the Web technology and the mobile devices have blurred the time and space boundaries of people’s daily activities, which enable people to work, entertain, and learn through the mobile device at almost anytime and anywhere. Together with the life-long learning requirement, such technology developments give birth to a new learning style, micro learning. Micro learning aims to effectively utilise learners’ fragmented spare time and carry out personalised learning activities. However, the massive volume of users and the online learning resources force the micro learning system deployed in the context of enormous and ubiquitous data. Hence, manually managing the online resources or user information by traditional methods are no longer feasible. How to utilise computational intelligence based solutions to automatically managing and process different types of massive information is the biggest research challenge for realising the micro learning service. As a result, to facilitate the micro learning service in the big data era efficiently, we need an intelligent system to manage the online learning resources and carry out different analysis tasks. To this end, an intelligent micro learning system is designed in this thesis. The design of this system is based on the service logic of the micro learning service. The micro learning system consists of three intelligent modules: learning material pre-processing module, learning resource delivery module and the intelligent assistant module. The pre-processing module interprets the content of the raw online learning resources and extracts key information from each resource. The pre-processing step makes the online resources ready to be used by other intelligent components of the system. The learning resources delivery module aims to recommend personalised learning resources to the target user base on his/her implicit and explicit user profiles. The goal of the intelligent assistant module is to provide some evaluation or assessment services (such as student dropout rate prediction and final grade prediction) to the educational resource providers or instructors. The educational resource providers can further refine or modify the learning materials based on these assessment results

From Filter Paper to Functional Actuator by Poly(ionic liquid)-Modified Graphene Oxide

A commercially available membrane filter paper composed of mixed cellulose esters bearing typically an interconnected pore structure was transformed into a stimuli-responsive bilayer actuator by depositing a thin film of poly(ionic liquid)-modified graphene oxide sheets (GO-PIL) onto the filter paper. In acetone vapor, the as-synthesized bilayer actuator bent readily into multiple loops at a fast speed with the GO-PIL top film inwards. Upon pulling back into air the actuator recovered their original shape. The asymmetric swelling of the top GO-PIL film and the bottom porous filter paper towards organic vapor offers a favorably synergetic function to drive the actuation. The PIL polymer chains in the hybrid film were proven crucial to enhance the adhesion strength between the GO sheets and the adjacent filter paper to avoid interfacial delamination and thus improve force transfer. The overall construction allows a prolonged lifetime of the bilayer actuator under constant operation, especially when compared to that of the GO/filter paper bilayer actuator without PIL.Comment: 23 pages, 7 figure

El impacto de los animales salvajes en la imagen percibida de los destinos turísticos-El caso del Monte Emei

El monte Emei está ubicado en Sichuan y es un destino nacional de nivel 5 A. En los últimos años, los frecuentes conflictos entre los monos salvajes en el destino y los visitantes han provocado discusiones acaloradas entre los turistas. Tomando el monte Emei como objeto de la investigación, he extraído los comentarios relacionados con los monos salvajes en Ctrip.com y he utilizado el análisis de comentarios para explorar el impacto de los monos salvajes en la imagen turística del monte Emei. Los resultados muestran que los turistas tienen emociones positivas sobre el paisaje natural y cultural del monte Emei, pero emociones negativas sobre los monos salvajes, lo que genera un impacto negativo en la imagen de marca turística del monte Emei, y la voluntad de los turistas de volver a visitarlo es baja. Con base a esta conclusión, este documento presenta sugerencias para mejorar la imagen turística general del Monte Emei: 1) Contratar profesionales para criar monos salvajes para mejorar la abundancia de alimentos de los monos salvajes; 2) Alertar a los turistas a mantener una cierta distancia de monos salvajes; 3) Controlar razonablemente el flujo de personas en este destino para lograr el propósito del desarrollo sostenible.<br /

Attention-based High-order Feature Interactions to Enhance the Recommender System for Web-based Knowledge-Sharing Servic

Providing personalized online learning services has become a hot research topic. Online knowledge-sharing services represents a popular approach to enable learners to use fragmented spare time. User asks and answers questions in the platform, and the platform also recommends relevant questions to users based on their learning interested and context. However, in the big data era, information overload is a challenge, as both online learners and learning resources are embedded in data rich environment. Offering such web services requires an intelligent recommender system to automatically filter out irrelevant information, mine underling user preference, and distil latent information. Such a recommender system needs to be able to mine complex latent information, distinguish differences between users efficiently. In this study, we refine a recommender system of a prior work for web-based knowledge sharing. The system utilizes attention-based mechanisms and involves high-order feature interactions. Our experimental results show that the system outperforms known benchmarks and has great potential to be used for the web-based learning service

Determination of dezocine in rabbit plasma by liquid chromatography-mass spectrometry and its application

A sensitive and selective liquid chromatography-mass spectrometry (LC–MS) method for determination of dezocine in rabbit plasma was developed and validated. After addition of diazepam as internal standard (IS), liquid–liquid extraction (LLE) was used for sample preparation, and chromatography involved Agilent SB-C18 column (2.1 mmx50 mm, 3.5 um) using 0.1 % formic acid in water and acetonitrile as a mobile phase with gradient elution. Detection involved positive ion mode electrospray ionization (ESI), and selective ion monitoring (SIM) mode was used for quantification of target fragment ions m/z 245.8 for dezocine and m/z 284.8 for diazepam (internal standard, IS). The assay was linear over the range of 5–500 ng/mL for dezocine, with a lower limit of quantitation (LLOQ) of 5 ng/mL for dezocine. Intra- and inter-day precisions were less than 13 % and the accuracies were in the range of 93.1-105.2 % for dezocine. This developed method was successfully applied for the determination of dezocine in rabbit plasma for pharmacokinetic study.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Prediction of standard-dose brain PET image by using MRI and low-dose brain [ 18 F]FDG PET images: Prediction of standard-dose brain PET image

Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [18F]FDG PET image by using a low-dose brain [18F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image