Hybrid computer Monte-Carlo techniques

Hybrid analog-digital computer systems for Monte Carlo method application

Graphene-protein bioelectronic devices with wavelength-dependent photoresponse

We implemented a nanoelectronic interface between graphene field effect transistors (FETs) and soluble proteins. This enables production of bioelectronic devices that combine functionalities of the biomolecular and inorganic components. The method serves to link polyhistidine-tagged proteins to graphene FETs using the tag itself. Atomic Force Microscopy and Raman spectroscopy provide structural understanding of the bio/nano hybrid; current-gate voltage measurements are used to elucidate the electronic properties. As an example application, we functionalize graphene FETs with fluorescent proteins to yield hybrids that respond to light at wavelengths defined by the optical absorption spectrum of the proteinComment: 10 pages, 3 figures; To appear in Applied Physics Letter

Improving SIEM for critical SCADA water infrastructures using machine learning

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset

International Public Health Research Involving Interpreters: a Case Study from Bangladesh

Background: Cross-cultural and international research are important components of public health research, but the challenges of language barriers and working with interpreters are often overlooked, particularly in the case of qualitative research. Methods: A case-study approach was used to explore experiences of working with an interpreter in Bangladesh as part of a research project investigating women's experiences of emergency obstetric care. The case study: Data from the researcher's field notes provided evidence of experiences in working with an interpreter and show how the model of interviewing was adapted over time to give a more active role to the interpreter. The advantages of a more active role were increased rapport and "flow" in interviews. The disadvantages included reduced control from the researcher's perspective. Some tensions between the researcher and interpreter remained hard to overcome, irrespective of the model used. Independent transcription and translation of the interviews also raised questions around accuracy in translation. Conclusion: The issues examined in this case study have broader implications for public health research. Further work is needed in three areas: 1) developing effective relationships with interpreters; 2) the impact of the interpreter on the research process; and 3) the accuracy of the translation and level of analysis needed in any specific public health research. Finally, this paper highlights the importance to authors of reflecting on the potential impact of translation and interpretation on the research process when disseminating their research

Predictive Model for Human-Unmanned Vehicle Systems

Advances in automation are making it possible for a single operator to control multiple unmanned vehicles. However, the complex nature of these teams presents a difficult and exciting challenge for designers of human–unmanned vehicle systems. To build such systems effectively, models must be developed that describe the behavior of the human–unmanned vehicle team and that predict how alterations in team composition and system design will affect the system’s overall performance. In this paper, we present a method for modeling human–unmanned vehicle systems consisting of a single operator and multiple independent unmanned vehicles. Via a case study, we demonstrate that the resulting models provide an accurate description of observed human-unmanned vehicle systems. Additionally, we demonstrate that the models can be used to predict how changes in the human-unmanned vehicle interface and the unmanned vehicles’ autonomy alter the system’s performance.Lincoln Laborator

Pre- and Post Impoundment Ichthyoparasite Succession in a New Arkansas Reservoir

Helminth and crustacean parasites from 2,387 Micropterus dolomieui, M. punctulatus, and M. salmoides were utilized to monitor annual pre- and postimpoundment succession patterns spanning eight con- tinuous years in Beaver Reservoir, Arkansas. Incidence of infection by ichthyoparasites with direct life cycles (monogenetic trematodes, leeches, and crustaceans) generally increased following impoundment, although leeches remained relatively constant. Exceptions to this general pattern occurred. Incidence of ichthyoparasites with indirect life cycles (digenetic trematodes, cestodes, acanthocephalans, and nematodes) decreased immediately following impoundment with subsequent increases to a point equal or above that of preimpoundment, although exceptions occurred. Time for species adaptation to the reservoir environment varied, with some species disappearing and others occurring for the first time. Diversity indices indicated that a moderate parasite community was maintained in the White River two years prior to its impoundment to form Beaver Reservoir. During the first impoundment year the parasite community declined to the lowest postimpoundment level with the abrupt change in habitat. Throughout the following four post- impoundment years the parasite community gradually increased to become much larger and more complex than it was during preimpoundment. Parasite community succession stabilization occurred in the fifth postimpoundment year and continued the following year indicating the establishment of a climax ichthyoparasite community

Small, Low-energy, Dispersive Solar Energetic Particle Events Observed by Parker Solar Probe

The Energetic Particle Instrument–Low Energy (EPI-Lo) experiment has detected several weak, low-energy (~30–300 keV nucleon⁻¹) solar energetic particle (SEP) events during its first two closest approaches to the Sun, providing a unique opportunity to explore the sources of low-energy particle acceleration. As part of the Parker Solar Probe (PSP) Integrated Science Investigation of the Sun (IS⊙IS) suite, EPI-Lo was designed to investigate the physics of energetic particles; however, in the special lowest-energy "time-of-flight only" product used in this study, it also responds to solar photons in a subset of approximately sunward-looking apertures lacking special light-attenuating foils. During the first three perihelia, in a frame rotating with the Sun, PSP undergoes retrograde motion, covering a 17° heliographic longitudinal range three times during the course of the ~11-day perihelion passes, permitting a unique spatial and temporal study into the location, correlation, and persistence of previously unmeasurable SEPs. We examine the signatures of these SEPs (during the first PSP perihelion pass only) and the connection to possible solar sources using remote observations from the Solar Dynamics Observatory (SDO), the Solar TErrestrial RElations Observatory (STEREO), and the ground-based Global Oscillation Network Group (GONG). The orientation of the Sun relative to STEREO, SDO, and GONG makes such identifications challenging, but we do have several candidates, including an equatorial coronal hole at a Carrington longitude of ~335°. To analyze observations from EPI-Lo, which is a new type of particle instrument, we examine instrumental effects and provide a preliminary separation of the ion signal from the photon background

A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-

Socially sensitive lactation: Exploring the social context of breastfeeding

Many women report difficulties with breastfeeding and do not maintain the practice for as long as intended. Although psychologists and other researchers have explored some of the difficulties they experience, fuller exploration of the relational contexts in which breastfeeding takes place is warranted to enable more in-depth analysis of the challenges these pose for breastfeeding women. The present paper is based on qualitative data collected from 22 first-time breastfeeding mothers through two phases of interviews and audio-diaries which explored how the participants experienced their relationships with significant others and the wider social context of breastfeeding in the first five weeks postpartum. Using a thematic analysis informed by symbolic interactionism, we develop the overarching theme of ‘Practising socially sensitive lactation’ which captures how participants felt the need to manage tensions between breastfeeding and their perceptions of the needs, expectations and comfort of others. We argue that breastfeeding remains a problematic social act, despite its agreed importance for child health. Whilst acknowledging the limitations of our sample and analytic approach, we suggest ways in which perinatal and public health interventions can take more effective account of the social challenges of breastfeeding in order to facilitate the health and psychological well-being of mothers and their infants