Journey to work:exploring difficulties, solutions and the impact of aging

A study was conducted in the UK, as part of the New Dynamics of Ageing Working Late project, of the journey to work among 1215 older workers (age groups 45-49, 50-55, 56-60 and 60 + ). The aim was to identify problems or concerns that they might have with their commute, strategies that have been adopted to address them, and the role that employers can play to assist them. Follow-up interviews with 36 employees identified many strategies for assisting with the problems of journeys to work, ranging from car share and using public transport to flexible working and working some days from home. Further interviews with a sample of 12 mainly larger companies showed that employers feel a responsibility for their workers’ commute, with some offering schemes to assist them, such as adjusting work shift timings to facilitate easier parking. The research suggests that the journey to work presents difficulties for a significant minority of those aged over 45, including issues with cost, stress, health, fatigue and journey time. It may be possible to reduce the impact of these difficulties on employee decisions to change jobs or retire by assisting them to adopt mitigating strategies. It does not appear that the likelihood of experiencing a problem with the journey to work increases as the employee approaches retirement; therefore, any mitigating strategy is likely to help employees of all ages. These strategies have been disseminated to a wider audience through an online resource at www.workinglate.org.

Evolutionary neural networks : models and applications

The scaling problems which afflict attempts to optimise neural networks (NNs) with genetic algorithms (GAs) are disclosed. A novel GA-NN hybrid is introduced, based on the bumptree, a little-used connectionist model. As well as being computationally efficient, the bumptree is shown to be more amenable to genetic coding lthan other NN models. A hierarchical genetic coding scheme is developed for the bumptree and shown to have low redundancy, as well as being complete and closed with respect to the search space. When applied to optimising bumptree architectures for classification problems the GA discovers bumptrees which significantly out-perform those constructed using a standard algorithm. The fields of artificial life, control and robotics are identified as likely application areas for the evolutionary optimisation of NNs. An artificial life case-study is presented and discussed. Experiments are reported which show that the GA-bumptree is able to learn simulated pole balancing and car parking tasks using only limited environmental feedback. A simple modification of the fitness function allows the GA-bumptree to learn mappings which are multi-modal, such as robot arm inverse kinematics. The dynamics of the 'geographic speciation' selection model used by the GA-bumptree are investigated empirically and the convergence profile is introduced as an analytical tool. The relationships between the rate of genetic convergence and the phenomena of speciation, genetic drift and punctuated equilibrium arc discussed. The importance of genetic linkage to GA design is discussed and two new recombination operators arc introduced. The first, linkage mapped crossover (LMX) is shown to be a generalisation of existing crossover operators. LMX provides a new framework for incorporating prior knowledge into GAs.Its adaptive form, ALMX, is shown to be able to infer linkage relationships automatically during genetic search.

Taking the carbon out of the car

Effective measures are being taken to reduce emissions from cars, which are now emerging as a major contributor to climate change. Developed countries will need to reduce emissions by at least 80% by 2050 to achieve stabilization of atmospheric CO2 concentration between 450 and 550 ppm, and have a unique opportunity to avoid the most damaging effects of climate change. The UK is aiming at completely decarbonising transport by 2050 through a combination of more efficient vehicles, cleaner fuels, and smart driving choices. The European Commission has proposed a mandatory CO2 target on new car CO 2 efficiency, which is an urgent needed development. The nation is also using regulatory targets for local schemes, such as free parking or congestion charging, break points for company car tax, and vehicle excise duty. Car ownership and use should thereby continue to drive economic growth and enhance quality of life around the world without destroying the planet.

QoE-aware power management in vehicle-to-grid networks:a matching-theoretic approach

Frequency, time and places of charging and discharging have critical impact on the Quality of Experience (QoE) of using Electric Vehicles (EVs). EV charging and discharging scheduling schemes should consider both the QoE of using EV and the load capacity of the power grid. In this paper, we design a traveling plan-aware scheduling scheme for EV charging in driving pattern and a cooperative EV charging and discharging scheme in parking pattern to improve the QoE of using EV and enhance the reliability of the power grid. For traveling planaware scheduling, the assignment of EVs to Charging Stations (CSs) is modeled as a many-to-one matching game and the Stable Matching Algorithm (SMA) is proposed. For cooperative EV charging and discharging in parking pattern, the electricity exchange between charging EVs and discharging EVs in the same parking lot is formulated as a many-to-many matching model with ties, and we develop the Pareto Optimal Matching Algorithm (POMA). Simulation results indicates that the SMA can significantly improve the average system utility for EV charging in driving pattern, and the POMA can increase the amount of electricity offloaded from the grid which is helpful to enhance the reliability of the power grid.