The Systems Theory Framework of career development: Applications to career counselling and career assessment

Since its inception, the Systems Theory Framework of career development has afforded ready translation into practice, especially into career counselling and qualitative career assessment. Through its clearly articulated constructs and the clarity of its diagrammatic representation, the Systems Theory Framework has facilitated the development of qualitative career assessment instruments as well as a quantitative measure. This article briefly overviews these practical applications of the Systems Theory Framework as well as its application in career counselling through a story telling approach. The article concludes by offering a synthesis of and considering future directions for the Systems Theory Framework’s practical applications.

An open source service oriented Mobile Business Intelligence Tool (MBIT)

Corporate executives require relevant and intelligent business information in real-time to take strategic decisions. They require the freedom to access this information anywhere and anytime. There is a need to extend this functionality beyond the office and on the fingertips of the decision makers. Mobile Business Intelligence Tool (MBIT) aims to provide these features in a flexible and cost-efficient manner. This paper describes the detailed architecture of MBIT to overcome the limitations of existing mobile business intelligence tools. Further, a detailed implementation framework is presented to realize the design. This research highlights the benefits of using service oriented architecture to design flexible and platform independent mobile business applications. © 2009 IEEE.

Mining topical relevant patterns for multi-document summarization

Multi-document summarization addressing the problem of information overload has been widely utilized in the various real-world applications. Most of existing approaches adopt term-based representation for documents which limit the performance of multi-document summarization systems. In this paper, we proposed a novel pattern-based topic model (PBTMSum) for the task of the multi-document summarization. PBTMSum combining pattern mining techniques with LDA topic modelling could generate discriminative and semantic rich representations for topics and documents so that the most representative and non-redundant sentences can be selected to form a succinct and informative summary. Extensive experiments are conducted on the data of document understanding conference (DUC) 2007. The results prove the effectiveness and efficiency of our proposed approach.

A cloud-based intelligent computing system for contextual exploration on personal sleep-tracking data using association rule mining

With the development of wearable and mobile computing technology, more and more people start using sleep-tracking tools to collect personal sleep data on a daily basis aiming at understanding and improving their sleep. While sleep quality is influenced by many factors in a person’s lifestyle context, such as exercise, diet and steps walked, existing tools simply visualize sleep data per se on a dashboard rather than analyse those data in combination with contextual factors. Hence many people find it difficult to make sense of their sleep data. In this paper, we present a cloud-based intelligent computing system named SleepExplorer that incorporates sleep domain knowledge and association rule mining for automated analysis on personal sleep data in light of contextual factors. Experiments show that the same contextual factors can play a distinct role in sleep of different people, and SleepExplorer could help users discover factors that are most relevant to their personal sleep.

Predicting single-layer technetium dichalcogenides (TcX2, X = S, Se) with promising applications in photovoltaics and photocatalysis

One of the least known compounds among transition metal dichalcogenides (TMDCs) is the layered triclinic technetium dichalcogenides (TcX2, X = S, Se). In this work, we systematically study the structural, mechanical, electronic, and optical properties of TcS2 and TcSe2 monolayers based on density functional theory (DFT). We find that TcS2 and TcSe2 can be easily exfoliated in a monolayer form because their formation and cleavage energy are analogous to those of other experimentally realized TMDCs monolayer. By using a hybrid DFT functional, the TcS2 and TcSe2 monolayers are calculated to be indirect semiconductors with band gaps of 1.91 and 1.69 eV, respectively. However, bilayer TcS2 exhibits direct-bandgap character, and both TcS2 and TcSe2 monolayers can be tuned from semiconductor to metal under effective tensile/compressive strains. Calculations of visible light absorption indicate that 2D TcS2 and TcSe2 generally possess better capability of harvesting sunlight compared to single-layer MoS2 and ReSe2, implying their potential as excellent light-absorbers. Most interestingly, we have discovered that the TcSe2 monolayer is an excellent photocatalyst for splitting water into hydrogen due to the perfect fit of band edge positions with respect to the water reduction and oxidation potentials. Our predictions expand the two-dimensional (2D) family of TMDCs, and the remarkable electronic/optical properties of monolayer TcS2 and TcSe2 will place them among the most promising 2D TMDCs for renewable energy application in the future.

An investigation into the thermophysical and rheological properties of nanofluids for solar thermal applications

Considered to be the next generation of heat transfer fluids, nanofluids have been receiving a growing amount of attention in the past decade despite the controversy and inconsistencies that have been reported. Nanofluids have great potential in a wide range of fields, particularly for solar thermal applications. This paper presents a comprehensive review of the literature on the enhancements in thermophysical and rheological properties resulting from experimental works conducted on molten salt nanofluids that are used in solar thermal energy systems. It was found that an increase in specific heat of 10–30% was achieved for most nanofluids and appeared independent of particle size and to an extent mass concentration. The specific heat increase was attributed to the formation of nanostructures at the solid–liquid interface and it was also noted that the aggregation of nanoparticles has detrimental effects on the specific heat increase. Thermal conductivity was also found to increase, though less consistently, ranging from 3% to 35%. Viscosity was seen to increase with the addition of nanoparticles and is dependent on the amount of aggregation of the particles. An in-depth micro level analysis of the mechanisms behind the thermophysical property changes is presented in this paper. In addition, possible trends are discussed relating to current theorised mechanisms in an attempt to explain the behaviour of molten salt nanofluids.

Performance estimation of streaming applications for hierarchical MPSoCs

Parallel programming and effective partitioning of applications for embedded many-core architectures requires optimization algorithms. However, these algorithms have to quickly evaluate thousands of different partitions. We present a fast performance estimator embedded in a parallelizing compiler for streaming applications. The estimator combines a single execution-based simulation and an analytic approach. Experimental results demonstrate that the estimator has a mean error of 2.6% and computes its estimation 2848 times faster compared to a cycle accurate simulator.

Extending visual perception with haptic exploration for improved scene understanding

Scene understanding has been investigated from a mainly visual information point of view. Recently depth has been provided an extra wealth of information, allowing more geometric knowledge to fuse into scene understanding. Yet to form a holistic view, especially in robotic applications, one can create even more data by interacting with the world. In fact humans, when growing up, seem to heavily investigate the world around them by haptic exploration. We show an application of haptic exploration on a humanoid robot in cooperation with a learning method for object segmentation. The actions performed consecutively improve the segmentation of objects in the scene.

Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment

This paper describes a concept for a collision avoidance system for ships, which is based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, compliance with the Convention on the International Regulations for Preventing Collisions at Sea and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.