Hydrogen production by oxidative steam reforming of methanol over anodic aluminum oxide-supported Cu-Zn catalyst

Oxidative steam reforming of methanol (OSRM), which is a convenient reaction for producing hydrogen, suffers from the hot spot formation problem when conventional particle catalysts are used. Recently, an anodic aluminum oxide (AAO)-supported Cu-Zn catalyst was proposed as an OSRM catalyst for its high thermal conductivity through the aluminum metal body. In this study, OSRM was conducted in a prototype reactor packed with the AAO plate catalyst strips. It was verified that the high thermal conductivity of the catalyst effectively suppresses the hot spot formation and makes the temperature profile smooth along the reactor. The start-up time of the reactor depended on the preheating temperature and was very short (less than 2 min) for preheating over 503 K. The methanol conversion and reactor temperature increased with increasing O₂/CH₃OH mole ratio, indicating that the mole ratio can be used as a control variable to operate the reactor at desired conditions. Further, a reactor model was developed and verified, and the simulation showed that for a given total reactor volume, an optimal reactor configuration could be achieved by shortening the reactor length while widening the cross-sectional area.     

Potential pitfalls of smart city development: A study on parking mobile applications (apps) in Hong Kong

Smart cities are built upon information and communication technologies (ICTs) to enable a broad range of advanced services. Through a comprehensive literature review, this study identified four pitfalls brought by the pervasive application of ICT, including information insecurity, privacy leakage, information islands, and digital divide. Therefore, a questionnaire survey together with 27 interviews was conducted in Hong Kong to investigate how the public perceived these pitfalls within the context of mobile apps providing real-time parking information which form a major part of smart mobility. System insecurity and privacy leakage were found to arouse worries among the app-users while their awareness of protecting personal data was found to have room for improvement. Islands of real-time parking information occur as a result of the lack of collaboration among private carpark operators. Digital divide existed widely among the disadvantaged groups and the problem cannot be solved by mere provision of ICT facilities. Overall, technologies alone cannot make a city smart or smarter. It is the suitable way in which ICTs are used to serve all citizens that matters.     

Why do people purchase virtual goods? A uses and gratification (U&G) theory perspective

Few literature studies have investigated the relationships between different uses and gratifications (U&Gs) of mobile instant messaging (MIM) apps, continuation, and purchase intentions. To address this gap, the researchers aimed to examine the influence of the content, social, process, and technology U&Gs of MIM on continuation intentions toward MIMs, and purchase intentions toward virtual goods available on MIMs. A comprehensive research model was developed based on the U&G theory, which was tested using cross-sectional data from 309 Japanese MIM users. The study considered six different U&Gs of MIM as independent variables and purchase intentions towards stickers and continuation intentions towards MIM as dependent variables. The study results suggest that exposure U&G has a significant positive association with MIM sticker purchase intentions. The entertainment and affection U&G are positively associated with continuation intentions towards MIM use. The study contributes to the literature by investigating U&Gs that motivate MIM users to have both positive purchase intentions toward virtual goods, such as stickers, and continuation intentions toward MIMs. The study has significant theoretical and practical implications for both researchers and practitioners who are interested in virtual goods, the virtual economy, MIM apps, social media, new media, and the service economy.     

A Differentially Private Data Aggregation Method Based on Worker Partition and Location Obfuscation for Mobile Crowdsensing

With the popularity of sensor-rich mobile devices, mobile crowdsensing (MCS) has emerged as an effective method for data collection and processing. However, MCS platform usually need workers’ precise locations for optimal task execution and collect sensing data from workers, which raises severe concerns of privacy leakage. Trying to preserve workers’ location and sensing data from the untrusted MCS platform, a differentially private data aggregation method based on worker partition and location obfuscation (DP-DAWL method) is proposed in the paper. DP-DAWL method firstly use an improved K-means algorithm to divide workers into groups and assign different privacy budget to the group according to group size (the number of workers). Then each worker’s location is obfuscated and his/her sensing data is perturbed by adding Laplace noise before uploading to the platform. In the stage of data aggregation, DP-DAWL method adopts an improved Kalman filter algorithm to filter out the added noise (including both added noise of sensing data and the system noise in the sensing process). Through using optimal estimation of noisy aggregated sensing data, the platform can finally gain better utility of aggregated data while preserving workers’ privacy. Extensive experiments on the synthetic datasets demonstrate the effectiveness of the proposed method.     

Efficient framework for mobile walkthrough application

The past few years have witnessed a dramatic growth in the number and variety of graphics intensive mobile applications, which allow users to interact and navigate through large scenes such as historical sites, museums and virtual cities. These applications support many clients and impose a heavy requirement on network resources and computational resources. One key issue in the design of cost efficient mobile walkthrough applications is the data transmission between servers and mobile client devices. In this paper, we propose an effective progressive mesh transmission framework that stores and divide scene objects into different resolutions. In this approach, each mobile device progressively receives and processes only the object’s details matching its display resolution which improves the overall system’s response time and the user’s perception. A fine grained cache mechanism is used to keep the most frequently requested objects’ details in the device memory and consequently reduce the network traffic. Experiments, in simulated and real world environment, are used to illustrate the effectiveness of the proposed framework under various settings of the virtual scene and mobile device configuration. Experimental results show that the proposed framework can improve the walkthrough system performance in mobile devices, with a relatively small overhead.     

Cloud asset for urban flood control

The performance of physical assets has become a major determinant success factor for urban flood control. However, managing these assets is always challenging as there are a huge number of diverse assets involved, which are distributed throughout the city, and owned by different agencies. Aiming at improving the management efficiency of these assets, and ensuring their performance, this paper proposes the concept of cloud asset based on cloud computing, mobile agent, and various smart devices. Through hardware integration and software encapsulation, cloud asset could sense its real-time status, adapt to varied working scenarios, be controlled remotely, and shared among agencies. It enables accurate real-time control of every asset, and thus improves the management efficiency and effectiveness. This paper first presents the concept of cloud asset with its technical architecture, and then analyses the software agent model for cloud asset, which is the key enabler to realize UPnP (Universal Plug and Play) management of assets, and provides mobility and intelligence for them. After that, the framework of cloud asset-enabled workflow management is built, in which cloud asset could be easily found and dynamically invoked by different workflows. Finally, a demonstrative case is provided to verify the effectiveness of cloud asset.     

Learning fuzzy controllers in mobile robotics with embedded preprocessing

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.     

Understanding trust in ms-commerce: The roles of reported experience,linguistic style,profile photo,emotional, and cognitive trust

This paper examines what influences trust in mobile social commerce environment. Drawing on trust-based acceptance model (i.e. cognitive and emotional trust) and online review features (i.e. profile photo, linguistic style, and reported experience), we examine how these factors affect trust in mobile social commerce. Hypotheses were tested using survey data. The results of our model showed that there are significant influences of profile photo, reported experience, cognitive, and emotional trust towards trust in ms-commerce. This work contributes to existing literature by examining the roles of previous trust in mobile payments and online reviews on trust in mobile social commerce.     

A Reference Architecture to support the development of mobile applications based on self-adaptive services

Nowadays, most human daily tasks can be performed by means of Mobile Applications (MobApps). Devices running such applications have some limitations (e.g., processing and storage) compared to personal computers. Therefore, integration of MobApps into service-based systems has been a feasible alternative to overcome these limitations. Moreover, these applications must be prepared to deal with the changes at runtime (e.g., user’s new needs or modifications in their execution environment). In parallel, Reference Architectures (RA) have been used as an important way to support the development, standardization, and evolution of software systems. Although relevant, RA for the domain of MobApps based on services that require adaptation at runtime is still an issue to be explored in depth. This paper presents a RA for Self-MobApps (named RA4Self-MobApps), which aims to support the development of such applications. To show the applicability of our RA, a case study was conducted. As result, we observe our RA has good perspective to efficiently contribute to the Self-MobApps domain.     

Mapping gender and marital roles on customer delight by value perception for mobile technology in India

The Indian telephony industry is a diverse platform for product and service in customer value perception. The present study organizes and synthesizes the varied research streams for developing a customer value delight (CVD) framework for marketing decisions like segmentation and targeting. The conceptual framework is developed by using ten-value predictor variables and customer delight through divergent viewpoints identified from previous studies. The significance of the segregation proposition is tested through discriminant analysis in three different dimension runs i.e. delight, gender, and marital category. The scholarship behind the study provides insight into an imperative issue of segregating customer delight by perceived value predictors in categories. The perceptual mapping and vector magnitude analysis of value predictors were performed to club customers in two categories of Mid-Town (low/moderate delight) and Extremes (high delight). The analysis rigorously supports the discriminating power of perceived price, perceived benefit, and self-congruity. The salience of study unlocks psychographic and demographic perceptual maps of customer delight with its perceived value predictors for strategic and tactful planning in the mobile industry.