Repository UNSAP (Universitas Sebelas April)
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Prévision et gestion des crues par opérations préventives sur les retenues alpines
No full textPL-LCH[506
Programmable microfluidic patterning of protein gradients on hydrogels
No full textComputer-controlled hydrodynamic flow focusing was utilized to generate tethered protein gradients of any user-defined shape on the surface of soft synthetic hydrogels.LDCSUPLU
Preliminary metadata paper. Data Domotopia: a web-based survey about home-making in a moving world.
No full textThis questionnaire Data Domotopia is a 100+ multi-purpose items about home-making and stillness in a moving world. The survey covers most of the contemporary issues related to home-making. This includes the domestic space and gender issues; the socio-spatial resources (mobility, action space, core, and wider social network); lifestyles, ideals, and residential aspiration; time pressures, time use, organization and stress; equipment, rules and arrangements; interpersonal relations, cohabitation and negotiation, dominance and power.LASU
Evolution of the Influenza A Virus Genome during Development of Oseltamivir Resistance In Vitro
No full textInfluenza A virus (IAV) is a major cause of morbidity and mortality throughout the world. Current antiviral therapies include oseltamivir, a neuraminidase inhibitor that prevents the release of nascent viral particles from infected cells. However, the IAV genome can evolve rapidly, and oseltamivir resistance mutations have been detected in numerous clinical samples. Using an in vitro evolution platform and whole-genome population sequencing, we investigated the population genomics of IAV during the development of oseltamivir resistance. Strain A/Brisbane/59/2007 (H1N1) was grown in Madin-Darby canine kidney cells with or without escalating concentrations of oseltamivir over serial passages. Following drug treatment, the H274Y resistance mutation fixed reproducibly within the population. The presence of the H274Y mutation in the viral population, at either a low or a high frequency, led to measurable changes in the neuraminidase inhibition assay. Surprisingly, fixation of the resistance mutation was not accompanied by alterations of viral population diversity or differentiation, and oseltamivir did not alter the selective environment. While the neighboring K248E mutation was also a target of positive selection prior to H274Y fixation, H274Y was the primary beneficial mutation in the population. In addition, once evolved, the H274Y mutation persisted after the withdrawal of the drug, even when not fixed in viral populations. We conclude that only selection of H274Y is required for oseltamivir resistance and that H274Y is not deleterious in the absence of the drug. These collective results could offer an explanation for the recent reproducible rise in oseltamivir resistance in seasonal H1N1 IAV strains in humans.UPJENSE
Low cycle fatigue and creep-fatigue of Eurofer 97 (EFDA technology workprogramme 2003 - Final report on task TW3-TTMS-05/Del No. 1)
No full textCRPPSP
Trends in robot-assisted and virtual reality-assisted neuromuscular therapy: a systematic review of health-related multiplayer games
No full textBackground: Multiplayer games have emerged as a promising approach to increase the motivation of patients involved in rehabilitation therapy. In this systematic review, we evaluated recent publications in health-related multiplayer games that involved patients with cognitive and/or motor impairments. The aim was to investigate the effect of multiplayer gaming on game experience and game performance in healthy and non-healthy populations in comparison to individual game play. We further discuss the publications within the context of the theory of flow and the challenge point framework. Methods: A systematic search was conducted through EMBASE, Medline, PubMed, Cochrane, CINAHL and PsycINFO. The search was complemented by recent publications in robot-assisted multiplayer neurorehabilitation. The search was restricted to robot-assisted or virtual reality-based training. Results: Thirteen articles met the inclusion criteria. Multiplayer modes used in health-related multiplayer games were: competitive, collaborative and co-active multiplayer modes. Multiplayer modes positively affected game experience in nine studies and game performance in six studies. Two articles reported increased game performance in single-player mode when compared to multiplayer mode. Conclusions: The multiplayer modes of training reviewed improved game experience and game performance compared to single-player modes. However, the methods reviewed were quite heterogeneous and not exhaustive. One important take-away is that adaptation of the game conditions can individualize the difficulty of a game to a player’s skill level in competitive multiplayer games. Robotic assistance and virtual reality can enhance individualization by, for example, adapting the haptic conditions, e.g. by increasing haptic support or by providing haptic resistance. The flow theory and the challenge point framework support these results and are used in this review to frame the idea of adapting players’ game conditions.NCCR-ROBOTIC
Livret "La Sensi à l'information Literacy"
No full textPrenez la route avec nous pour découvrir l'Information Literacy. En suivant ce parcours de sensibilisation, vous découvrirez les différents enjeux liés à la maîtrise de l'information.VPA-SIS
Ni-Catalyzed Cross Coupling of Non-Activated and Functionalized Alkyl Halides with Alkyl Grignard Reagents
No full textThe title reaction is realized by using an isolated NiII complex (1). The catalysis tolerates a wide range of important functional groups that are often incompatible with Grignard reagents in cross-coupling reactions.LSC
A survey on policy search algorithms for learning robot controllers in a handful of trials
No full textMost policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word "big-data", we refer to this challenge as "micro-data reinforcement learning". We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time.LIDIA