CO2 abatement by co-firing of natural gas and biomass-derived gas in a gas turbine

In this work, a possible way for partial CO₂ emissions reduction from gas turbine exhausts by co-firing with biomass is investigated. The basic principle is the recirculation of a fraction of the exhausts (still rich in oxygen) to a gasifier, in order to produce syngas to mix with natural gas fuel. As biomass is a CO₂ neutral fuel, the fraction of replaced natural gas is a measure of CO₂ removal potential of the powerplant.     

Chemically vapour deposited diamond coatings on cemented tungsten carbides: Substrate pretreatments, adhesion and cutting performance

Chemical vapour deposition (CVD) of diamond films onto Co-cemented tungsten carbide (WC-Co) tools and wear parts presents several problems due to interfacial graphitization induced by the binder phase and thermal expansion mismatch of diamond and WC-Co. Methods used to improve diamond film adhesion include substrate-modification processes that create a three-dimensional compositionally graded interface. This paper reviews substrate pretreatments and adhesion issues of chemically vapour deposited diamond films on WC-Co. The combined effect of pretreatments and substrate microstructure on the adhesive toughness and wear rate of CVD diamond in dry machining of highly abrasive materials was analyzed. The role of diamond film surface morphology on chip evacuation in dry milling of ceramics was also investigated by comparing feed forces of coated and uncoated mills. The overall tribological performance of diamond coated mills depended on coating microstructure and smoothness. The use of smother films did allow to reduce cutting forces by facilitating chip evacuation.     

Detecting similarities in virtual machine behavior for cloud monitoring using smoothed histograms

The growing size and complexity of cloud systems determine scalability issues for resource monitoring and management. While most existing solutions consider each Virtual Machine (VM) as a black box with independent characteristics, we embrace a new perspective where VMs with similar behaviors in terms of resource usage are clustered together. We argue that this new approach has the potential to address scalability issues in cloud monitoring and management. In this paper, we propose a technique to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. This innovative technique models VMs behavior exploiting the probability histogram of their resources usage, and performs smoothing-based noise reduction and selection of the most relevant information to consider for the clustering process. Through extensive evaluation, we show that our proposal achieves high and stable performance in terms of automatic VM clustering, and can reduce the monitoring requirements of cloud systems.     

Evolving the Topology and the Weights of Neural Networks Using a Dual Representation

Evolutionary computation is a class of global search techniques based on the learning process of a population of potential solutions to a given problem, that has been successfully applied to a variety of problems. In this paper a new approach to the construction of neural networks based on evolutionary computation is presented. A linear chromosome combined to a graph representation of the network are used by genetic operators, which allow the evolution of the architecture and the weights simultaneously without the need of local weight optimization. This paper describes the approach, the operators and reports results of the application of this technique to several binary classification problems.     

Solving job shop scheduling with setup times through constraint-based iterative sampling: an experimental analysis

This paper presents a heuristic algorithm for solving a job-shop scheduling problem with sequence dependent setup times and min/max separation constraints among the activities (SDST-JSSP/max). The algorithm relies on a core constraint-based search procedure, which generates consistent orderings of activities that require the same resource by incrementally imposing precedence constraints on a temporally feasible solution. Key to the effectiveness of the search procedure is a conflict sampling method biased toward selection of most critical conflicts and coupled with a non-deterministic choice heuristic to guide the base conflict resolution process. This constraint-based search is then embedded within a larger iterative-sampling search framework to broaden search space coverage and promote solution optimization. The efficacy of the overall heuristic algorithm is demonstrated empirically both on a set of previously studied job-shop scheduling benchmark problems with sequence dependent setup times and by introducing a new benchmark with setups and generalized precedence constraints.     

Using 3D Sound to Improve the Effectiveness of the Advanced Driver Assistance Systems

Future generation cars will be characterized by a wide range of Information Technology (IT) services providing safety and infotainment. This makes the car an information intensive environment where the visual channel is overloaded, putting the safety of drivers and passengers in jeopardy. We propose the use of a 3D auditory display to provide information from the Advanced Driver Assistance Systems. This reduces the eye-off-road time, exploiting the human capability to associate sounds with positions in space. Preliminary lab tests reveal the suitability of this approach. The system still has to be carefully tuned and personalized to achieve usability and reliability, but we think that it provides a complementary channel that is specially useful in low visibility conditions.     

Identification and analysis of isothiocyanates and new acylated anthocyanins in the juice of Raphanus sativus cv. Sango sprouts

The freeze-dried sprouts’ juice of Raphanus sativus (L.) cv. Sango was prepared and analysed for the first time. HPLC analysis of total isothiocyanates, after protein displacement, resulted in 77.8 ± 3.0 μmol/g of dry juice while GC–MS analysis of hexane and acetone extracts showed E- and Z-raphasatin (8.9 and 0.11 μmol/g, respectively) and sulforaphene (11.7 μmol/g), summing up to 20.7 ± 1.7 μmol/g of free isothiocyanates. Sprouts’ juice contained an unprecedented wealth of anthocyanins and a new fractionation methodology allowed us to isolate 34 mg/g of acylated anthocyanins (28.3 ± 1.9 μmol/g), belonging selectively to the cyanidin family. Analysis was performed by HPLC–PDA–ESI–MSⁿ and extended to deacylated anthocyanins and aglycones, obtained, respectively, by alkaline and acid hydrolysis. This study identified 70 anthocyanins, 19 of which have never been described before and 32 of which are reported here in R. sativus for the first time. Sango radish sprouts are exceptional dietary sources of heath-promoting micronutrients.     

Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon

Artificial Neural Networks (ANNs) have been successfully employed for predicting and forecasting groundwater levels up to some time steps ahead. In this paper, we present an application of feed forward neural networks (FFNs) for long period simulations of hourly groundwater levels in a coastal unconfined aquifer sited in the Lagoon of Venice, Italy. After initialising the model with groundwater elevations observed at a given time, the developed FNN should able to reproduce water level variations using only the external input variables, which have been identified as rainfall and evapotranspiration. To achieve this purpose, the models are first calibrated on a training dataset to perform 1-h ahead predictions of future groundwater levels using past observed groundwater levels and external inputs. Simulations are then produced on another data set by iteratively feeding back the predicted groundwater levels, along with real external data. The results show that the developed FNN can accurately reproduce groundwater depths of the shallow aquifer for several months. The study suggests that such network can be used as a viable alternative to physical-based models to simulate the responses of the aquifer under plausible future scenarios or to reconstruct long periods of missing observations provided past data for the influencing variables is available.     

Exact Schema Theory for Genetic Programming and Variable-Length Genetic Algorithms with One-Point Crossover

A few schema theorems for genetic programming (GP) have been proposed in the literature in the last few years. Since they consider schema survival and disruption only, they can only provide a lower bound for the expected value of the number of instances of a given schema at the next generation rather than an exact value. This paper presents theoretical results for GP with one-point crossover which overcome this problem. First, we give an exact formulation for the expected number of instances of a schema at the next generation in terms of microscopic quantities. Due to this formulation we are then able to provide an improved version of an earlier GP schema theorem in which some (but not all) schema creation events are accounted for. Then, we extend this result to obtain an exact formulation in terms of macroscopic quantities which makes all the mechanisms of schema creation explicit. This theorem allows the exact formulation of the notion of effective fitness in GP and opens the way to future work on GP convergence, population sizing, operator biases, and bloat, to mention only some of the possibilities.