Mobile healthcare information systems : thru' the ant lens

Deployment of mobile technologies and applications in health care are becoming prevalent worldwide. As mobile innovation and standards in wireless continue to evolve, so does the mobile health care information systems framework. In this paper, we explore this evolving framework by synthesising current technologies, applications, issues and examining this through the actor-network theory (ANT) which seeks to understand socio-technical change by adopting a symmetric treatment of people and technologies.

The relationship between emergence of the shortest path and information value using ant-like agents

Ant-like agents forage between two points. These agents' probabilistic movements are based on the use of two pheromones; one marking trails towards the goal and another marking trails back to the starting point. Path selection decisions are influenced by the relative levels of attractive and repulsive pheromone in each agent's local environment. Our work in [5] evaluates three pheromone perception strategies, investigating path formation speed, quality, directionality, robustness and adaptability under different parameter settings(degree of randomness, pheromone evaporation rate and pheromone diffusion rate). We re-evaluate two of these strategies in terms of the amount of information they provide using Shannon's formulation [3, 4, 8, 9, 12, 14, 15, 16, 17]. We determine information as the difference between uncertainty before and after path selection decisions. Our focus in this paper is on investigating relationships between the emergence of the shortest path and the amount of stigmergic information that exists in the form of pheromone. Agents are deployed centrally and emergence measures are determined using the worst, reference and best cases observed in [5]. Additionally, the amount of local and global information that is available to agents in each movement step is evaluated. Furthermore, Pearson's correlation coefficients between measures of emergence and the amount of information are calculated. The significance of these correlation coefficients is tested using a 2 tailed test at 1% level of significance. Consequently the relationship between the amount of information and emergent behaviour is established. Significant relationships between information and the emergence of the shortest path exist when strong emergent behaviour is present.

A probabilistic movement model for shortest path formation in virtual ant-like agents

We propose a probabilistic movement model for controlling ant-like agents foraging between two points. Such agents are all identical, simple, autonomous and can only communicate indirectly through the environment. These agents secrete two types of pheromone, one to mark trails towards the goal and another to mark trails back to the starting point. Three pheromone perception strategies are proposed (Strategy A, B and C). Agents that use strategy A perceive the desirability of a neighbouring location as the difference between levels of attractive and repulsive pheromone in that location. With strategy B, agents perceive the desirability of a location as the quotient of levels of attractive and repulsive pheromone. Agents using strategy C determine the product of the levels of attractive pheromone with the complement of levels of repulsive pheromone. We conduct experiments to confirm directionality as emergent property of trails formed by agents that use each strategy. In addition, we compare path formation speed and the quality of the formed path under changes in the environment. We also investigate each strategy's robustness in environments that contain obstacles. Finally, we investigate how adaptive each strategy is when obstacles are eventually removed from the scene and find that the best strategy of these three is strategy A. Such a strategy provides useful guidelines to researchers in further applications of swarm intelligence metaphors for complex problem solving.

An ant colony system based on the physarum network

The Physarum Network model exhibits the feature of important pipelines being reserved with the evolution of network during the process of solving a maze problem. Drawing on this feature, an Ant Colony System (ACS), denoted as PNACS, is proposed based on the Physarum Network (PN). When updating pheromone matrix, we should update both pheromone trails released by ants and the pheromones flowing in a network. This hybrid algorithm can overcome the low convergence rate and local optimal solution of ACS when solving the Traveling Salesman Problem (TSP). Some experiments in synthetic and benchmark networks show that the efficiency of PNACS is higher than that of ACS. More important, PNACS has strong robustness that is very useful for solving a higher dimension TSP.

Actor-Network Theory (ANT) based visualisation of socio-technical facets of RFID technology translation: an Australian hospital scenario

In the early 2000s, Information Systems researchers in Australia had begun to emphasise socio-technical approaches in innovation adoption of technologies. The ‘essentialist' approaches to adoption (for example, Innovation Diffusion or TAM), suggest an essence is largely responsible for rate of adoption (Tatnall, 2011) or a new technology introduced may spark innovation. The socio-technical factors in implementing an innovation are largely flouted by researchers and hospitals. Innovation Translation is an approach that purports that any innovation needs to be customised and translated in to context before it can be adopted. Equally, Actor-Network Theory (ANT) is an approach that embraces the differences in technical and human factors and socio-professional aspects in a non-deterministic manner. The research reported in this paper is an attempt to combined the two approaches in an effective manner, to visualise the socio-technical factors in RFID technology adoption in an Australian hospital. This research investigation demonstrates RFID technology translation in an Australian hospital using a case approach (Yin, 2009). Data was collected using a process of focus groups and interviews, analysed with document analysis and concept mapping techniques. The data was then reconstructed in a ‘movie script' format, with Acts and Scenes funnelled to ANT informed abstraction at the end of each Act. The information visualisation at the end of each Act using ANT informed Lens reveal the re-negotiation and improvement of network relationships between the people (factors) involved including nurses, patient care orderlies, management staff and non-human participants such as equipment and technology. The paper augments the current gaps in literature regarding socio-technical approaches in technology adoption within Australian healthcare context, which is transitioning from non-integrated nearly technophobic hospitals in the last decade to a tech-savvy integrated era. More importantly, the ANT visualisation addresses one of the criticisms of ANT i.e. its insufficiency to explain relationship formations between participants and over changes of events in relationship networks (Greenhalgh & Stones, 2010).

A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model

Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP.

A novel Physarum-Based ant colony system for solving the real-world traveling salesman problem

The solutions to Traveling Salesman Problem can be widely applied in many real-world problems. Ant colony optimization algorithms can provide an approximate solution to a Traveling Salesman Problem. However, most ant colony optimization algorithms suffer premature convergence and low convergence rate. With these observations in mind, a novel ant colony system is proposed, which employs the unique feature of critical tubes reserved in the Physaurm-inspired mathematical model. A series of experiments are conducted, which are consolidated by two realworld Traveling Salesman Problems. The experimental results show that the proposed new ant colony system outperforms classical ant colony system, genetic algorithm, and particle swarm optimization algorithm in efficiency and robustness.

A multi-objective ant colony optimization algorithm based on the physarum-inspired mathematical model

Multi-objective traveling salesman problem (MOTSP) is an important field in operations research, which has wide applications in the real world. Multi-objective ant colony optimization (MOACO) as one of the most effective algorithms has gained popularity for solving a MOTSP. However, there exists the problem of premature convergence in most of MOACO algorithms. With this observation in mind, an improved multiobjective network ant colony optimization, denoted as PMMONACO, is proposed, which employs the unique feature of critical tubes reserved in the network evolution process of the Physarum-inspired mathematical model (PMM). By considering both pheromones deposited by ants and flowing in the Physarum network, PM-MONACO uses an optimized pheromone matrix updating strategy. Experimental results in benchmark networks show that PM-MONACO can achieve a better compromise solution than the original MOACO algorithm for solving MOTSPs.

RFID translation into Australian hospitals: an exploration through Actor-Network Theoretical(ANT) Lens

Radio Frequency Identification (RFID) is a technology that has been deployed successfully for asset tracking within hospitals aimed at improving the quality of processes. In the Australian hospitals context however, adoption of this technology seem sporadic. This research reports on a long-term investigation to gain a deeper understanding of the socio-technical factors involved in the adoption of RFID in Australian hospitals. The research was conducted using interpretive multiple case methodology and results analyzed through the Actor-Network Theoretical (ANT) Lens. © 2013 Infonomics Society.

Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting

Wind farms are producing a considerable portion of the world renewable energy. Since the output power of any wind farm is highly dependent on the wind speed, the power extracted from a wind park is not always a constant value. In order to have a non-disruptive supply of electricity, it is important to have a good scheduling and forecasting system for the energy output of any wind park. In this paper, a new hybrid swarm technique (HAP) is used to forecast the energy output of a real wind farm located in Binaloud, Iran. The technique consists of the hybridization of the ant colony optimization (ACO) and particle swarm optimization (PSO) which are two meta-heuristic techniques under the category of swarm intelligence. The hybridization of the two algorithms to optimize the forecasting model leads to a higher quality result with a faster convergence profile. The empirical hourly wind power output of Binaloud Wind Farm for 364 days is collected and used to train and test the prepared model. The meteorological data consisting of wind speed and ambient temperature is used as the inputs to the mathematical model. The results indicate that the proposed technique can estimate the output wind power based on the wind speed and the ambient temperature with an MAPE of 3.513%.

Cryptococcus haglerorum, sp nov., an anamorphic basidiomycetous yeast isolated from nests of the leaf-cutting ant Atta sexdens

A yeast strain (CBS 8902) was isolated from the nest of a leaf-cutting ant and was shown to be related to Cryptococcus humicola. Sequencing of the D1/D2 region of the 26S ribosomal DNA and physiological characterization revealed a separate taxonomic position. A novel species named Cryptococcus haglerorum is proposed to accommodate strain CBS 8902 that assimilates n-hexadecane and several benzene compounds. Physiological characteristics distinguishing the novel species from some other members of the C. humicola complex are presented. The phylogenetic relationship of these strains to species of the genus Trichosporon Behrend is discussed.

Metabolism of plant polysaccharides by Leucoagaricus gongylophorus, the symbiotic fungus of the leaf-cutting ant Atta sexdens L.

Atta sexdens L, ante feed on the Fungus they cultivate on cut leaves inside their nests. The fungus, Leucoagaricus gongylophorus, metabolizes plant polysaccharides, such as xylan, starch, pectin, and cellulose, mediating assimilation of these compounds lay the ants, This metabolic integration may be an important part of the ant-fungus symbiosis, and it involves primarily xylan and starch, both of which support rapid fungal growth. Cellulose seems to be less important for symbiont nutrition, since it is poorly degraded and assimilated by the fungus. Pectin is rapidly degraded but slowly assimilated by L. gongylophorus, and its degradation may occur so that the fungus can more easily access other polysaccharides in the leaves.

Sympodiomyces attinorum sp nov., a yeast species associated with nests of the leaf-cutting ant Atta sexdens

Four strains of a novel yeast species were isolated from laboratory nests of the leaf-cutting ant Atta sexdens in Brazil. Three strains were found in older sponges and one was in a waste deposit in the ant nests. Sequencing of the D1/D2 region of the large-subunit rRNA gene showed that the novel species, named Sympodiomyces attinorum sp. nov., is phylogenetically related to Sympodiomyces parvus. Unlike Sympodiomyces parvus, Sympodiomyces attinorum can ferment glucose, assimilate methyl alpha-D-glucoside, salicin and citrate, and grow at 37 degreesC, thus enabling these two species to be distinguished. Differentiation from other related species is possible on the basis of other growth characteristics. The type strain of Sympodiomyces attinorum is UNESP-S156(T) (=CBS 9734(T)=NRRL Y-27639(T)).