HLA_ACTOR_REPAST: An approach to distributing RePast models for high-performance simulations

RePast is a popular agent toolkit with proven capabilities to fulfil the modelling challenges of large multi-agent systems (MASs). The toolkit, though, is normally used on a standalone workstation and therefore its practical use can be constrained (in space and time) by the limited available computing resources. This paper proposes an original approach – HLA_ACTOR_REPAST – aimed to distributing RePast models for high-performance simulation of complex scalable models. Novel in HLA_ACTOR_REPAST is an exploitation of a lean actor infrastructure implemented in Java. Actors bring to RePast agents such features as migration, location-transparent naming, efficient communications, and a control-centric framework. Actors can be orchestrated by an in-the-large custom control structure which is in charge of ensuring the necessary message causality constraints. Distribution and time management concerns depend on the IEEE standard HLA middleware. The paper first discusses details of the software engineering process underlying the development of HLA_ACTOR_REPAST. The mapping techniques, assisted by Java text annotations and aspect-oriented programming, try to minimize “code intrusions” in the original model and favour model transparency. The paper then furnishes some experimental data which witness the good performance results achieved by applying HLA_ACTOR_REPAST to a distributed version of a classic MAS benchmark model.     

Comprehensive Automation for Specialty Crops: Year 1 results and lessons learned

Comprehensive Automation for Specialty Crops is a project focused on the needs of the specialty crops sector, with a focus on apples and nursery trees. The project’s main thrusts are the integration of robotics technology and plant science; understanding and overcoming socio-economic barriers to technology adoption; and making the results available to growers and stakeholders through a nationwide outreach program. In this article, we present the results obtained and lessons learned in the first year of the project with a reconfigurable mobility infrastructure for autonomous farm driving. We then present sensor systems developed to enable three real-world agricultural applications—insect monitoring, crop load scouting, and caliper measurement—and discuss how they can be deployed autonomously to yield increased production efficiency and reduced labor costs.     

Proteomic analysis of secretory proteins and vesicles in vascular research

The release of proteins and membrane vesicles in the bloodstream regulates diverse vascular processes, both physiological, such as angiogenesis and haemostasis, and pathological, such as atherosclerosis and atherothrombosis. Proteomics, beside its canonical application for the expression profiling in cells and organs, can be applied to the study of secreted proteins and microvesicles, which play a significant role in the homeostasis of the vasculature, and the development of the atherosclerotic disease.     

Dissipation in vibrating superleak second sound transducers

We have performed an experimental study of the generation and detection of second sound in⁴He using vibrating superleak second sound transducers. At temperatures well belowT and for low driving amplitudes, the magnitude of the generated second sound wave is proportional to the drive amplitude. However, nearT and for high drive amplitudes this is no longer the case—instead, the second sound amplitude saturates. In this regime we also find that overtones of the drive frequency are generated. Our results suggest that this behavior is due to critical velocity effects in the pores of the superleak in the generator transducer. This type of measurement may prove to be a useful way in which to study critical velocity effects in confined geometries.     

An integrated computer-controlled system for assisting researchers in cortical excitability studies by using transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is the most important technique currently available to study cortical excitability. Additionally, TMS can be used for therapeutic and rehabilitation purposes, replacing the more painful transcranial electric stimulation (TES). In this paper we present an innovative and easy-to-use tool that enables neuroscientists to design, carry out and analyze scientific studies based on TMS experiments for both diagnostic and research purposes, assisting them not only in the practicalities of administering the TMS but also in each step of the entire study's workflow. One important aspect of this tool is that it allows neuroscientists to specify research designs at will, enabling them to define any parameter of a TMS study starting from data acquisition and sample group definition to automated statistical data analysis and RDF data storage. It also supports the diagnosing process by using on-line support vector machines able to learn incrementally from the diseases instances that are continuously added into the system. The proposed system is a neuroscientist-centred tool where the protocols being followed in TMS studies are made explicit, leaving to the users flexibility in exploring and sharing the results, and providing assistance in managing the complexity of the final diagnosis. This type of tool can make the results of medical experiments more easily exploitable, thus accelerating scientific progress.     

A methodology for screening of microalgae as a decision making tool for energy and green chemical process applications

The increasing interest for biotechnological use of microalgae demands a methodology for selection of species suitable to support the development of technologies based on the use of such non-conventional renewable raw material, i.e., green industrial applications. The vast and expanding collection of experimental data on both cell growth and biomass composition available in the literature can be used to reduce the cost of the experimental investigations required to support process engineering and optimization. Selecting the appropriate organism requires extracting useful information from such data, a cumbersome task since various multidisciplinary factors must be considered. This paper presents a computer-aided methodology for selecting appropriate algal species given an energy or green chemical process application employing microalgae as a renewable raw material. The approach is “system oriented”, based on biomass composition and chemical processing of the biomass downstream of the CO₂ biofixation and harvesting operations. Quantitative performance results are supported by professional process simulation. Besides comparison of a set of species performances, the proposed methodology also allows the discrimination among distinct algal compositions resulting from different growth conditions for a given species. Furthermore, three categories of screening metrics are proposed to be maximized by the decision making procedure in order to elicit the relevant information. To demonstrate the potential of the proposed methodology, a databank of both biochemical and elemental compositions of microalgal biomass was used in three green applications: Assessment of biomass heating value; production of syngas by gasification of the biomass; and production of Bio-H₂. Within the accuracy of the databank employed to illustrate the procedure, the methodology selected Botryococcus braunii and Isochrysis galbana as potential promising candidates, for the three examined applications.     

Effect of dry‐heat treatments on the nutritional value of maize germ

Maize germ is a by‐product of the maize milling process that is characterised by a high nutritional value. Currently, heat treatments are employed to prevent full‐fat maize germ from spoilage. The aim of this research was to study the effect of five dry‐heat treatments on the nutritional value of full‐fat maize germ. The results confirmed that after each dry‐heat treatment, the lipase activity decreases but the use of high temperatures could be detrimental for phytosterol and thiamine concentrations. The main negative effects have been observed after treatments at 140 °C for 30 min and 160 °C for 10 min. No significant difference has been observed for protein, ash or fatty acid contents. The treatment at 140 °C for 20 min resulted an optimal combination between temperature and heating time to inactivate lipase without altering deeply the nutritional value and the colour of maize germ.     

LEKIDs Developments for mm-Wave Astronomy

Microwave Kinetic Inductance Detectors (MKIDs) have recently drawn the attention of the low-temperature detectors community. Easy fabrication, high sensitivity, small time constants and most notably the intrinsic capability to frequency multiplexing open new possibilities to applications that need very large array sizes and/or high speed read-out. Lumped Element Kinetic Inductance Detectors (LEKIDs) designed and fabricated in our collaboration have already shown good on sky performances, but new developments are needed for future multi-thousands-pixels instruments. In this contribution we present such ongoing developments: a new LEKIDs design, optimized to be dual polarization sensitive; use of new materials such as TiN in order to reach better signal to noise ratios; new solutions to minimize the cross-talk between pixels in order to achieve a better control of the resonance positions in frequency space. We discuss present lab measurements of the optical performances and recent improvements of the read-out electronics.