Preparation of macroporous methacrylate monolithic material with convective flow properties for bioseparation : Investigating the kinetics of pore formation and hydrodynamic performance

The preparation of macroporous methacrylate monolithic material with controlled pore structures can be carried out in an unstirred mould through careful and precise control of the polymerisation kinetics and parameters. Contemporary synthesis conditions of methacrylate monolithic polymers are based on existing polymerisation schemes without an in-depth understanding of the dynamics of pore structure and formation. This leads to poor performance in polymer usage thereby affecting final product recovery and purity, retention time, productivity and process economics. The unique porosity of methacrylate monolithic polymer which propels its usage in many industrial applications can be controlled easily during its preparation. Control of the kinetics of the overall process through changes in reaction time, temperature and overall composition such as cross-linker and initiator contents allow the fine tuning of the macroporous structure and provide an understanding of the mechanism of pore formation within the unstirred mould. The significant effect of temperature of the reaction kinetics serves as an effectual means to control and optimise the pore structure and allows the preparation of polymers with different pore size distributions from the same composition of the polymerisation mixture. Increasing the concentration of the cross-linking monomer affects the composition of the final monoliths and also decreases the average pore size as a result of pre-mature formation of highly cross-linked globules with a reduced propensity to coalesce. The choice and concentration of porogen solvent is also imperative. Different porogens and porogen mixtures present different pore structure output. Example, larger pores are obtained in a poor solvent due to early phase separation.

Convergence-guaranteed time-varying RRT path planning for profiling floats in 4-Dimensional flow

This paper presents an extension to the Rapidly-exploring Random Tree (RRT) algorithm applied to autonomous, drifting underwater vehicles. The proposed algorithm is able to plan paths that guarantee convergence in the presence of time-varying ocean dynamics. The method utilizes 4-Dimensional, ocean model prediction data as an evolving basis for expanding the tree from the start location to the goal. The performance of the proposed method is validated through Monte-Carlo simulations. Results illustrate the importance of the temporal variance in path execution, and demonstrate the convergence guarantee of the proposed methods.

"I take care of my own" : a field study on how leadership handles conflict between individual and collective incentives

From the early literature on the role of firm managers (Alchian and Demsetz 1972) to the industrial organisation on contracts and mechanism design (Laont and Martimort 2009), economists have given a lot of attention to find solutions to the imperfect alignment between individuals' incentives and an organisation's collective goals (Prendergast 1999). In that literature a key role of managers is to monitor individuals to reward behaviour aligned with the collective goals and reduce sub- optimal behaviour, such as shirking. How- ever, another strand of literature, since Akerlof (1982), has put forward a vision of reciprocal behaviour between an organisation's leadership and its members: gifts (high wages, recognition) from the organisation are reciprocated by high effort from the members of the organisation. By rewarding individual members (rather than strictly monitoring them), organisations may benefit from greater effort and cohesion. Experimental research in organizational economics has provided mixed results suggesting that agents do react to personal incentives but also that reciprocal behaviour can play a substantial role (Camerer and Weber 2012).

On the use of optical flow for scene change detection and description

We propose the use of optical flow information as a method for detecting and describing changes in the environment, from the perspective of a mobile camera. We analyze the characteristics of the optical flow signal and demonstrate how robust flow vectors can be generated and used for the detection of depth discontinuities and appearance changes at key locations. To successfully achieve this task, a full discussion on camera positioning, distortion compensation, noise filtering, and parameter estimation is presented. We then extract statistical attributes from the flow signal to describe the location of the scene changes. We also employ clustering and dominant shape of vectors to increase the descriptiveness. Once a database of nodes (where a node is a detected scene change) and their corresponding flow features is created, matching can be performed whenever nodes are encountered, such that topological localization can be achieved. We retrieve the most likely node according to the Mahalanobis and Chi-square distances between the current frame and the database. The results illustrate the applicability of the technique for detecting and describing scene changes in diverse lighting conditions, considering indoor and outdoor environments and different robot platforms.

Multi-action recognition via stochastic modelling of optical flow and gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.

Rotational cryptanalysis of round-reduced Keccak

In this paper we attack round-reduced Keccak hash function with a technique called rotational cryptanalysis. We focus on Keccak variants proposed as SHA-3 candidates in the NIST’s contest for a new standard of cryptographic hash function. Our main result is a preimage attack on 4-round Keccak and a 5-round distinguisher on Keccak-f[1600] permutation — the main building block of Keccak hash function.

Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation

Wound healing and tumour growth involve collective cell spreading, which is driven by individual motility and proliferation events within a population of cells. Mathematical models are often used to interpret experimental data and to estimate the parameters so that predictions can be made. Existing methods for parameter estimation typically assume that these parameters are constants and often ignore any uncertainty in the estimated values. We use approximate Bayesian computation (ABC) to estimate the cell diffusivity, D, and the cell proliferation rate, λ, from a discrete model of collective cell spreading, and we quantify the uncertainty associated with these estimates using Bayesian inference. We use a detailed experimental data set describing the collective cell spreading of 3T3 fibroblast cells. The ABC analysis is conducted for different combinations of initial cell densities and experimental times in two separate scenarios: (i) where collective cell spreading is driven by cell motility alone, and (ii) where collective cell spreading is driven by combined cell motility and cell proliferation. We find that D can be estimated precisely, with a small coefficient of variation (CV) of 2–6%. Our results indicate that D appears to depend on the experimental time, which is a feature that has been previously overlooked. Assuming that the values of D are the same in both experimental scenarios, we use the information about D from the first experimental scenario to obtain reasonably precise estimates of λ, with a CV between 4 and 12%. Our estimates of D and λ are consistent with previously reported values; however, our method is based on a straightforward measurement of the position of the leading edge whereas previous approaches have involved expensive cell counting techniques. Additional insights gained using a fully Bayesian approach justify the computational cost, especially since it allows us to accommodate information from different experiments in a principled way.

Analysis of passenger group behaviour and its impact on passenger flow using an agent-based model

Group interaction within crowds is a common phenomenon and has great influence on pedestrian behaviour. This paper investigates the impact of passenger group dynamics using an agent-based simulation method for the outbound passenger process at airports. Unlike most passenger-flow models that treat passengers as individual agents, the proposed model additionally incorporates their group dynamics as well. The simulation compares passenger behaviour at airport processes and discretionary services under different group formations. Results from experiments (both qualitative and quantitative) show that incorporating group attributes, in particular, the interactions with fellow travellers and wavers can have significant influence on passengers activity preference as well as the performance and utilisation of services in airport terminals. The model also provides a convenient way to investigate the effectiveness of airport space design and service allocations, which can contribute to positive passenger experiences. The model was created using AnyLogic software and its parameters were initialised using recent research data published in the literature.

Impact of mobility and topology on information diffusion in MANETs

In some delay-tolerant communication systems such as vehicular ad-hoc networks, information flow can be represented as an infectious process, where each entity having already received the information will try to share it with its neighbours. The random walk and random waypoint models are popular analysis tools for these epidemic broadcasts, and represent two types of random mobility. In this paper, we introduce a simulation framework investigating the impact of a gradual increase of bias in path selection (i.e. reduction of randomness), when moving from the former to the latter. Randomness in path selection can significantly alter the system performances, in both regular and irregular network structures. The implications of these results for real systems are discussed in details.

Collective futures: How projections about the future of society are related to actions and attitudes supporting social change

We identified the active ingredients in people’s visions of society’s future (“collective futures”) that could drive political behavior in the present. In eight studies (N = 595), people imagined society in 2050 where climate change was mitigated (Study 1), abortion laws relaxed (Study 2), marijuana legalized (Study 3), or the power of different religious groups had increased (Studies 4-8). Participants rated how this future society would differ from today in terms of societal-level dysfunction and development (e.g., crime, inequality, education, technology), people’s character (warmth, competence, morality), and their values (e.g., conservation, self-transcendence). These measures were related to present-day attitudes/intentions that would promote/prevent this future (e.g., act on climate change, vote for a Muslim politician). A projection about benevolence in society (i.e., warmth/morality of people’s character) was the only dimension consistently and uniquely associated with present-day attitudes and intentions across contexts. Implications for social change theories, political communication, and policy design are discussed.

Achieving intention-centric BPM through automated planning

As a result of the more distributed nature of organisations and the inherently increasing complexity of their business processes, a significant effort is required for the specification and verification of those processes. The composition of the activities into a business process that accomplishes a specific organisational goal has primarily been a manual task. Automated planning is a branch of artificial intelligence (AI) in which activities are selected and organised by anticipating their expected outcomes with the aim of achieving some goal. As such, automated planning would seem to be a natural fit to the BPM domain to automate the specification of control flow. A number of attempts have been made to apply automated planning to the business process and service composition domain in different stages of the BPM lifecycle. However, a unified adoption of these techniques throughout the BPM lifecycle is missing. As such, we propose a new intention-centric BPM paradigm, which aims on minimising the specification effort by exploiting automated planning techniques to achieve a pre-stated goal. This paper provides a vision on the future possibilities of enhancing BPM using automated planning. A research agenda is presented, which provides an overview of the opportunities and challenges for the exploitation of automated planning in BPM.

An efficient DSE using conditional multivariate complex Gaussian distribution

This paper presents an efficient noniterative method for distribution state estimation using conditional multivariate complex Gaussian distribution (CMCGD). In the proposed method, the mean and standard deviation (SD) of the state variables is obtained in one step considering load uncertainties, measurement errors, and load correlations. In this method, first the bus voltages, branch currents, and injection currents are represented by MCGD using direct load flow and a linear transformation. Then, the mean and SD of bus voltages, or other states, are calculated using CMCGD and estimation of variance method. The mean and SD of pseudo measurements, as well as spatial correlations between pseudo measurements, are modeled based on the historical data for different levels of load duration curve. The proposed method can handle load uncertainties without using time-consuming approaches such as Monte Carlo. Simulation results of two case studies, six-bus, and a realistic 747-bus distribution network show the effectiveness of the proposed method in terms of speed, accuracy, and quality against the conventional approach.

Local Commons: A visual approach to collective city making through situated community engagement

Due to the numerous possibilities of voicing concerns and the flood of data we are exposed to, local issues are sometimes at risk of being overlooked. This study explores Local Commons, a design intervention in public space that combines situated digital and tangible media in order to engage communities in contributing and debating different perspectives on a given local issue. The intervention invited the community to submit images of their perspectives on the issue, which were displayed on a public screen. Via tangible buttons in front of the screen, community members then agree or disagree on the displayed perspectives, creating a space for deliberation. In a user study, we were specifically interested in testing three aspects of our intervention, which are discussed in this paper: The difference that situatedness, visual content, and tangible interaction can make to urban community engagement.

Numerical treatment of a two-dimensional variable-order fractional nonlinear reaction-diffusion model

A two-dimensional variable-order fractional nonlinear reaction-diffusion model is considered. A second-order spatial accurate semi-implicit alternating direction method for a two-dimensional variable-order fractional nonlinear reaction-diffusion model is proposed. Stability and convergence of the semi-implicit alternating direct method are established. Finally, some numerical examples are given to support our theoretical analysis. These numerical techniques can be used to simulate a two-dimensional variable order fractional FitzHugh-Nagumo model in a rectangular domain. This type of model can be used to describe how electrical currents flow through the heart, controlling its contractions, and are used to ascertain the effects of certain drugs designed to treat arrhythmia.