Improving fuel economy application of the organic Rankine Cycle on a hybrid bus

Globally vehicle operators are experiencing rising fuel costs and increased
running expenses as governments around the world attempt to decrease carbon dioxide emissions and fossil fuel consumption, due to global warming and the drive to reduce dependency on fossil fuels. Recent advances in hybrid vehicle design have made great strides towards more efficient operation, with regenerative braking being widely used to capture otherwise lost energy. In this paper a hybrid series bus is developed a step further, by installing another method of energy capture on the vehicle. In this case, it is in the form of the Organic Rankine Cycle (ORC). The waste heat expelled to the exhaust and coolant streams is recovered and converted to electrical energy which is then stored in the hybrid vehicles batteries. The electrical energy can then be used for the auxiliary power circuit or to assist in vehicle propulsion, thus reducing the load on the engine, thereby improving the overall fuel economy of the vehicle and reducing carbon dioxide emissions.

A Bayesian Network Hybrid Model for Representing Accident and Emergency Waiting Times

The paper introduces a new modeling approach that represents the waiting times in an Accident and Emergency (A&E) Department in a UK based National Health Service (NHS) hospital. The technique uses Bayesian networks to capture the heterogeneity of arriving patients by representing how patient covariates interact to influence their waiting times in the department. Such waiting times have been reviewed by the NHS as a means of investigating the efficiency of A&E departments (Emergency Rooms) and how they operate. As a result activity targets are now established based on the patient total waiting times with much emphasis on trolley waits.

A hybrid metaheuristic approach to the university course timetabling problem

This paper describes the development of a novel metaheuristic that combines an electromagnetic-like mechanism (EM) and the great deluge algorithm (GD) for the University course timetabling problem. This well-known timetabling problem assigns lectures to specific numbers of timeslots and rooms maximizing the overall quality of the timetable while taking various constraints into account. EM is a population-based stochastic global optimization algorithm that is based on the theory of physics, simulating attraction and repulsion of sample points in moving toward optimality. GD is a local search procedure that allows worse solutions to be accepted based on some given upper boundary or ‘level’. In this paper, the dynamic force calculated from the attraction-repulsion mechanism is used as a decreasing rate to update the ‘level’ within the search process. The proposed method has been applied to a range of benchmark university course timetabling test problems from the literature. Moreover, the viability of the method has been tested by comparing its results with other reported results from the literature, demonstrating that the method is able to produce improved solutions to those currently published. We believe this is due to the combination of both approaches and the ability of the resultant algorithm to converge all solutions at every search process.

Strategies for Energy-Efficient Resource Management of Hybrid Programming Models

Many scientific applications are programmed using hybrid programming models that use both message passing and shared memory, due to the increasing prevalence of large-scale systems with multicore, multisocket nodes. Previous work has shown that energy efficiency can be improved using software-controlled execution schemes that consider both the programming model and the power-aware execution capabilities of the system. However, such approaches have focused on identifying optimal resource utilization for one programming model, either shared memory or message passing, in isolation. The potential solution space, thus the challenge, increases substantially when optimizing hybrid models since the possible resource configurations increase exponentially. Nonetheless, with the accelerating adoption of hybrid programming models, we increasingly need improved energy efficiency in hybrid parallel applications on large-scale systems. In this work, we present new software-controlled execution schemes that consider the effects of dynamic concurrency throttling (DCT) and dynamic voltage and frequency scaling (DVFS) in the context of hybrid programming models. Specifically, we present predictive models and novel algorithms based on statistical analysis that anticipate application power and time requirements under different concurrency and frequency configurations. We apply our models and methods to the NPB MZ benchmarks and selected applications from the ASC Sequoia codes. Overall, we achieve substantial energy savings (8.74 percent on average and up to 13.8 percent) with some performance gain (up to 7.5 percent) or negligible performance loss.

Antiproton induced DNA damage: proton like in flight, carbon-ion like near rest

Biological validation of new radiotherapy modalities is essential to understand their therapeutic potential. Antiprotons have been proposed for cancer therapy due to enhanced dose deposition provided by antiproton-nucleon annihilation. We assessed cellular DNA damage and relative biological effectiveness (RBE) of a clinically relevant antiproton beam. Despite a modest LET (,19 keV/mm), antiproton spread out Bragg peak (SOBP) irradiation caused significant residual c-H2AX foci compared to X-ray, proton and antiproton plateau irradiation. RBE of ,1.48 in the SOBP and ,1 in the plateau were measured and used for a qualitative effective dose curve comparison with proton and carbon-ions. Foci in the antiproton SOBP were larger and more structured compared to X-rays, protons and carbon-ions. This is likely due to overlapping particle tracks near the annihilation vertex, creating spatially correlated DNA lesions. No biological effects were observed at 28–42 mm away from the primary beam suggesting minimal risk from long-range secondary particles.

Sugar–derived organogels as templates for structured, photoluminescent conjugated polymer-inorganic hybrid materials

Co-assembly of an inorganic–organic hybrid material through the combination of supramolecular organogel self-assembly, phase partitioning of a conjugated polymer (CP) and transcription of an inorganic oxide leads to a hybrid material with structured domains of organogel, CP and silica within tube and rod microstructures.

Farmer perception of suitable conditions for slurry application compared with decision support system recommendations

The application of slurry nutrients to land can be associated with unintended losses to the environment depending on soil and weather conditions. Correct timing of slurry application, however, can increase plant nutrient uptake and reduce losses. A decision support system (DSS), which predicts optimum conditions for slurry spreading based on the Hybrid Soil Moisture Deficit (HSMD) model, was investigated for use as a policy tool. The DSS recommendations were compared to farmer perception of suitable conditions for slurry spreading for three soil drainage classes (well, moderate and poorly drained) to better understand on farm slurry management practices and to identify potential conflict with farmer opinion. Six farmers participated in a survey over two and a half years, during which they completed a daily diary, and their responses were compared to Soil Moisture Deficit (SMD) calculations and weather data recorded by on farm meteorological stations. The perception of land drainage quality differed between farmers and was related to their local knowledge and experience. It was found that the allocation of grass fields to HSMD drainage classes using a visual assessment method aligned farmer perception of drainage at the national scale. Farmer opinion corresponded to the theoretical understanding that slurry should not be applied when the soil is wetter than field capacity, i.e. when drainage can occur. While weather and soil conditions (especially trafficability) were the principal reasons given by farmers not to spread slurry, farm management practices (grazing and silage) and current Nitrates Directive policies (closed winter period for spreading) combined with limited storage capacities were obstacles to utilisation of slurry nutrients. Despite the slightly more restrictive advice of the DSS regarding the number of suitable spreading opportunities, the system has potential to address an information deficit that would help farmers to reduce nutrient losses and optimise plant nutrient uptake by improved slurry management. The DSS advice was in general agreement with the farmers and, therefore, they should not be resistant to adopting the tool for day to day management.

Characterization of the AtsR Hybrid Sensor Kinase Phosphorelay Pathway and Identification of Its Response Regulator in Burkholderia cenocepacia

AtsR is a membrane-bound hybrid sensor kinase of Burkholderia cenocepacia that negatively regulates quorum sensing and virulence factors such as biofilm production, type 6-secretion and protease secretion. Here, we elucidate the mechanism of AtsR phosphorelay by site-directed mutagenesis of predicted histidine and aspartic acid phosphoacceptor residues. We demonstrate by in vitro phosphorylation that histidine-245 and aspartic acid-536 are conserved sites of phosphorylation in AtsR, and we also identify the cytosolic response regulator AtsT (BCAM0381) as a key component of the AtsR phosphorelay pathway. Monitoring the function of AtsR and its derivatives in vivo by measuring extracellular protease activity and swarming motility confirmed the in vitro phosphorylation results. Together, we find that the AtsR receiver domain plays a fine-tuning role in determining the levels of phosphotransfer from its sensor kinase domain to the AtsT response regulator.

The Engine of Thought Is a Hybrid: Roles of Associative and Structured Knowledge in Reasoning

Across a range of domains in psychology different theories assume different mental representations of knowledge. For example, in the literature on category-based inductive reasoning, certain theories (e.g., Rogers & McClelland, 2004; Sloutsky & Fisher, 2008) assume that the knowledge upon which inductive inferences are based is associative, whereas others (e.g., Heit & Rubinstein, 1994; Kemp & Tenenbaum, 2009; Osherson, Smith, Wilkie, López, & Shafir, 1990) assume that knowledge is structured. In this article we investigate whether associative and structured knowledge underlie inductive reasoning to different degrees under different processing conditions. We develop a measure of knowledge about the degree of association between categories and show that it dissociates from measures of structured knowledge. In Experiment 1 participants rated the strength of inductive arguments whose categories were either taxonomically or causally related. A measure of associative strength predicted reasoning when people had to respond fast, whereas causal and taxonomic knowledge explained inference strength when people responded slowly. In Experiment 2, we also manipulated whether the causal link between the categories was predictive or diagnostic. Participants preferred predictive to diagnostic arguments except when they responded under cognitive load. In Experiment 3, using an open-ended induction paradigm, people generated and evaluated their own conclusion categories. Inductive strength was predicted by associative strength under heavy cognitive load, whereas an index of structured knowledge was more predictive of inductive strength under minimal cognitive load. Together these results suggest that associative and structured models of reasoning apply best under different processing conditions and that the application of structured knowledge in reasoning is often effortful.

Hybrid address spaces: A methodology for implementing scalable high-level programming models on non-coherent many-core architectures

This paper introduces hybrid address spaces as a fundamental design methodology for implementing scalable runtime systems on many-core architectures without hardware support for cache coherence. We use hybrid address spaces for an implementation of MapReduce, a programming model for large-scale data processing, and the implementation of a remote memory access (RMA) model. Both implementations are available on the Intel SCC and are portable to similar architectures. We present the design and implementation of HyMR, a MapReduce runtime system whereby different stages and the synchronization operations between them alternate between a distributed memory address space and a shared memory address space, to improve performance and scalability. We compare HyMR to a reference implementation and we find that HyMR improves performance by a factor of 1.71× over a set of representative MapReduce benchmarks. We also compare HyMR with Phoenix++, a state-of-art implementation for systems with hardware-managed cache coherence in terms of scalability and sustained to peak data processing bandwidth, where HyMR demon- strates improvements of a factor of 3.1× and 3.2× respectively. We further evaluate our hybrid remote memory access (HyRMA) programming model and assess its performance to be superior of that of message passing.