A Hardware Acceleration Scheme for Memory-Efficient Flow Processing

This paper presents a hardware solution for network flow processing at full line rate. Advanced memory architecture using DDR3 SDRAMs is proposed to cope with the flow match limitations in packet throughput, number of supported flows and number of packet header fields (or tuples) supported for flow identifications. The described architecture has been prototyped for accommodating 8 million flows, and tested on an FPGA platform achieving a minimum of 70 million lookups per second. This is sufficient to process internet traffic flows at 40 Gigabit Ethernet.

A Cross-Layer Optimized Scheme and Its Application in Mobile Multimedia Networks with QoS Provision

To cope with the rapid growth of multimedia applications that requires dynamic levels of quality of service (QoS), cross-layer (CL) design, where multiple protocol layers are jointly combined, has been considered to provide diverse QoS provisions for mobile multimedia networks. However, there is a lack of a general mathematical framework to model such CL scheme in wireless networks with different types of multimedia classes. In this paper, to overcome this shortcoming, we therefore propose a novel CL design for integrated real-time/non-real-time traffic with strict preemptive priority via a finite-state Markov chain. The main strategy of the CL scheme is to design a Markov model by explicitly including adaptive modulation and coding at the physical layer, queuing at the data link layer, and the bursty nature of multimedia traffic classes at the application layer. Utilizing this Markov model, several important performance metrics in terms of packet loss rate, delay, and throughput are examined. In addition, our proposed framework is exploited in various multimedia applications, for example, the end-to-end real-time video streaming and CL optimization, which require the priority-based QoS adaptation for different applications. More importantly, the CL framework reveals important guidelines as to optimize the network performance

Thermography-based virtual MPPT scheme for improving PV energy efficiency under partial shading conditions

This paper proposes a new thermography-based maximum power point tracking (MPPT) scheme to address photovoltaic (PV) partial shading faults. Solar power generation utilizes a large number of PV cells connected in series and in parallel in an array, and that are physically distributed across a large field. When a PV module is faulted or partial shading occurs, the PV system sees a nonuniform distribution of generated electrical power and thermal profile, and the generation of multiple maximum power points (MPPs). If left untreated, this reduces the overall power generation and severe faults may propagate, resulting in damage to the system. In this paper, a thermal camera is employed for fault detection and a new MPPT scheme is developed to alter the operating point to match an optimized MPP. Extensive data mining is conducted on the images from the thermal camera in order to locate global MPPs. Based on this, a virtual MPPT is set out to find the global MPP. This can reduce MPPT time and be used to calculate the MPP reference voltage. Finally, the proposed methodology is experimentally implemented and validated by tests on a 600-W PV array. 

Activity and coke formation of nickel and nickel carbide in dry reforming: A deactivation scheme from density functional theory

Dry reforming is a promising reaction to utilise the greenhouse gases CO2 and CH4. Nickel-based catalysts are the most popular catalysts for the reaction, and the coke formation on the catalysts is the main obstacle to the commercialisation of dry reforming. In this study, the whole reaction network of dry reformation on both flat and stepped nickel catalysts (Ni(111) and Ni(211)) as well as nickel carbide (flat: Ni3C(001); stepped: Ni3C(111)) is investigated using density functional theory calculations. The overall reaction energy profiles in the free energy landscape are obtained, and kinetic analyses are utilised to evaluate the activity of the four surfaces. By careful examination of our results, we find the following regarding the activity: (i) flat surfaces are more active than stepped surfaces for the dry reforming and (ii) metallic nickel catalysts are more active than those of nickel carbide, and therefore, the phase transformation from nickel to nickel carbide will reduce the activity. With respect to the coke formation, the following is found: (i) the coke formation probability can be measured by the rate ratio of CH oxidation pathway to C oxidation pathway (r(CH)/r(C)) and the barrier of CO dissociation, (ii) on Ni(111), the coke is unlikely to form, and (iii) the coke formations on the stepped surfaces of both nickel and nickel carbide can readily occur. A deactivation scheme, using which experimental results can be rationalised, is proposed. 

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.

Participation of Smallholders in Agroforestry Agri-Environmental Scheme: A lesson from the rural Mount Kenya region

The likelihood of smallholder farmers not participating in agroforestry agri-environmental schemes and payments for ecosystem services (PES) may be due to limited farmland endowment and formal credit constraints. These deficits may lead to an ‘exclusive club’ of successful farmers, which are not necessarily poor, enjoying the benefits of agri-environmental schemes and PES although agrienvironmental schemes and PES have been devised as a means of fostering rural sustainable development and improving the livelihood of poor smallholder farmers. Smallholder farmers in parts of rural Kenya continue to enroll in ‘The International Small Group Tree Planting Programme’ (TIST), an agri-environmental scheme, promoting agroforestry, carbon sequestration and conservation agriculture (CA). The question remains if these farmers are really poor? This study examines factors that determine the participation of smallholder farmers in TIST in parts of rural Kenya. We use survey data compiled in 2013 on 210 randomly selected smallholder farmers from Embu, Meru and Nanyuki communities; the sample consists of TIST and non-TIST members. A random utility model and logit regression were used to test a set of non-monetary and monetary factors that influence participation in the TIST. The utility function is conceptualized to give non-monetary factors, particularly the common medium of communication in rural areas – formal and informal – a central role. Furthermore, we investigate other factors (incl. credit accessibility and interest rate) that reveal the nature of farmers participating in TIST. The findings suggest that spread of information via formal and informal networks is a major driver of participation in the TIST program. Furthermore, variables such credit constrains, age and labour supply positively correlate with TIST participation, while for education the opposite is true. It is important to mention that these correlations, although somewhat consistent, were all found to be weak. The results indicate that participation in the TIST program is not influenced by farm size; therefore we argue that the TIST scheme is NOT an ‘exclusive club’ comprising wealthy and successful farmers. Older farmers’ being more likely to join the TIST is an argument for their long- rather than widely assumed short-term planning horizon and a new contribution to the literature. Given the importance of poverty alleviation and climate smart agriculture in developing countries, sustainable policy should strengthening the social and human capital as well as informal networks in rural areas. Extension services should effectively communicate benefits to less educated and credit constrained farmers.

A Scheme Design of Cloud + End Technology in Demand Side Management

Demand Side Management (DSM) plays an important role in Smart Grid. It has large scale access points, massive users, heterogeneous infrastructure and dispersive participants. Moreover, cloud computing which is a service model is characterized by resource on-demand, high reliability and large scale integration and so on and the game theory is a useful tool to the dynamic economic phenomena. In this study, a scheme design of cloud + end technology is proposed to solve technical and economic problems of the DSM. The architecture of cloud + end is designed to solve technical problems in the DSM. In particular, a construct model of cloud + end is presented to solve economic problems in the DSM based on game theories. The proposed method is tested on a DSM cloud + end public service system construction in a city of southern China. The results demonstrate the feasibility of these integrated solutions which can provide a reference for the popularization and application of the DSM in china.

Incentives and rewards: what do adult populations truly want? Insights from the physical activity loyalty (PAL) scheme

Background
Trials depend on good recruitment and retention, but efforts to improve these have had varying success. This may be due to inadequate understanding of what participants would value in return for taking part. An opportunity arose in one trial to investigate the incentives that might help recruit and retain participants to another.

Aim
To determine what adults value as an incentive for involvement in a trial.

Methods
In the PAL Scheme, employees used a ‘loyalty card’ to monitor their physical activity over 12 weeks. The incentive group (n=199) collected points and received rewards for physical activity (1 minute = 1 point, max: 30 pts/day). A comparator group (n=207) self-monitored their physical activity only. Points could be redeemed as retail vouchers. 17 different incentives were available, from 75 pts (£2.50, a sandwich) to 1800 pts (£60, 1 month gym membership).

Results
148 of the 199 intervention participants used their card at least once, earning a mean of 374 pts. 121 earned sufficient to collect a reward and 76 redeemed points for vouchers but only 48 exchanged the vouchers for rewards. The most popular reward was not that of highest monetary value: two cinema tickets (300 pts, £10).

Conclusions
The value that participants place on a reward might be more important than its monetary value. Some might appreciate receiving the voucher, without spending it. In choosing incentives to boost trial participation, it may help to allow people to choose from a variety of rewards, rather than reimbursing in money.