INFORMATION SOCIETY: NEW CHALLENGES FOR SUSTAINABLE DEVELOPMENT

The overall aim of this study is to identify the strengths and the opportunities,the weaknesses and the threats of information society that could enable the national and theinternational economies to achieve continuous improvement of quality of life both for currentand future generations, through the creation of sustainable communities able to manageresources efficiently and to tap the ecological and social innovation potential of the economy,ensuring prosperity, environmental protection, social cohesion and cultural diversity. Theauthors of this paper mean to harness the vast potential of ICTs to achieve the sustainabledevelopment aspirations, but also to pay attention to the limits of information society’sextending. Recognizing the importance of ICT as valuable assets for economic growth anddevelopment, world leaders should align their efforts towards building a development-oriented information society.Cuvinte cheie: sustainable development, e-commerce, e-government, cultural diversity

Hordozós nanorészecskék morfológiájának szerepe szelektív hidrogénezési reakciókban = Role of the morphology of supported nanoparticles in selective hydrogenation reactions

A pályázatban leírt kutatási feladat megoldásában az alábbi eredmények születtek. 1. Kidolgoztunk módszereket különböző morfológiájú nemesfém nanorészecskék szintézisére vizes közegben. A felületvédő anyagok közül a PVP és a NIPA bizonyult általánosan alkalmazhatónak. 2. Kidolgoztuk, hogyan célszerű a nemesfém nanorészecskéket tartalmazó szuszpenziókból a fémkomponenst szilárd hordozó felületére deponálni. 3. Bizonyítottuk, hogy a hordozó anyag és a katalitikusan aktív komponens egyaránt lehetnek nanométer méretűek. 4. Bizonyítottuk, hogy a szén nanocsöveken hordozott FeOx/CoOx nanorészecskék jó aktivitást mutatnak a CO hidrogénezésében. 5. A titanát nanocsövek és nanoszálak szintézisére új módszert dolgoztunk ki; erről és az alkalmazott eszközről szabadalmi bejelentést tettünk. 6. Bizonyítottuk a titanát nanocsöveken hordozott Pt nanorészecskék katalitikus aktivitását ciklohexén hidrogénezésében. 7. Titanát nanocsöveken CdS nanorészecskéket deponálva olyan fotokatalitikusan aktív katalizátort állítottunk elő, amely a látható fény tartományában működik. 8. A titanát nanocsövek polimerkémiai hasznosításával is foglalkoztunk; ez nem esett közvetlenül a pályázat témakörébe, de szerettük volna széleskörben alkalmazni ismereteinket. 9. Új szintézismódszert dolgoztunk ki nagy fajlagos felületű (1600 m2/g) mezopórusos szenek szintézisére. 10. Új szintézismódszert dolgoztunk ki Pt és Rh nanorészecskék szintézisére mezopórusos szén hordozón. | At the end of the project, we can summarize the results. 1. We developed a new method for the size- and shape controlled synthesis of noble metal nanocrystals in aqueous media. Out of the several capping agents tried in the synthesis procedures PVP and NIPA were found to be generally applicable. 2. We described a new method to put the noble metal nanocrystals onto the surface of different solid supports. 3. We proved using experimental data that both the catalytically active components and the supports can be nanometer sized. 4. We proved that catalysts containing pre-prepared iron oxide and cobalt oxide nanoparticles supported on CNTs show high activity in the CO hydrogenation reaction (FT synthesis). 5. We developed a new method for the synthesis of trititanate nanotubes. We applied for a patent. 6. We have shown that the catalyst containing Pt nanoparticles supported on trititanate nanotubes was active in the cyclohexene hydrogenation reaction. 7. We synthesized CdS-containing trititanate nanotubes. We were able to prove that these materials are photocatalytically active in visible light. 8. We developed new trititanate/polymer composites (though this topic was not strongly related to the project). 9. We developed the synthesis of a new, high surface area mesoporous carbon family with specific surface area up to 1600 m2/g. 10. We developed a new synthesis route for the preparation of Pt and Rh containing mesoporous carbon materials

Effects of hippocampal damage on reward threshold and response rate during self-stimulation of the ventral tegmental area in the rat

The main purpose of this study was to explore the role of the hippocampus in motivated behavior. Rats with bilateral excitotoxic lesions of the hippocampus and controls were trained to lever press for electrical stimulation of the ventral tegmental area. Rate intensity functions were generated from an ascending and descending series of current intensities. Lesion-induced changes in sensitivity to reward were distinguished from enhancements in motor output by calculating reward thresholds and maximal response rates from the rate-intensity functions. Rats with hippocampal damage showed lower reward thresholds and higher maximal response rates than controls. These results provide further evidence of hippocampal modulation of the nucleus accumbens, suggesting that lesions of this structure enhance sensitivity to reward and increase motor output

Genetic inhibition of neurotransmission reveals role of glutamatergic input to dopamine neurons in high-effort behavior

Midbrain dopamine neurons are crucial for many behavioral and cognitive functions. As the major excitatory input, glutamatergic afferents are important for control of the activity and plasticity of dopamine neurons. However, the role of glutamatergic input as a whole onto dopamine neurons remains unclear. Here we developed a mouse line in which glutamatergic inputs onto dopamine neurons are specifically impaired, and utilized this genetic model to directly test the role of glutamatergic inputs in dopamine-related functions. We found that while motor coordination and reward learning were largely unchanged, these animals showed prominent deficits in effort-related behavioral tasks. These results provide genetic evidence that glutamatergic transmission onto dopaminergic neurons underlies incentive motivation, a willingness to exert high levels of effort to obtain reinforcers, and have important implications for understanding the normal function of the midbrain dopamine system.Fil: Hutchison, M. A.. National Institutes of Health; Estados UnidosFil: Gu, X.. National Institutes of Health; Estados UnidosFil: Adrover, Martín Federico. National Institutes of Health; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; ArgentinaFil: Lee, M. R.. National Institutes of Health; Estados UnidosFil: Hnasko, T. S.. University of California at San Diego; Estados UnidosFil: Alvarez, V. A.. National Institutes of Health; Estados UnidosFil: Lu, W.. National Institutes of Health; Estados Unido

Activation of Ventral Tegmental Area 5-HT2C Receptors Reduces Incentive Motivation

FUNDING AND DISCLOSURE The research was funded by Wellcome Trust (WT098012) to LKH; and National Institute of Health (DK056731) and the Marilyn H. Vincent Foundation to MGM. The University of Michigan Transgenic Core facility is partially supported by the NIH-funded University of Michigan Center for Gastrointestinal Research (DK034933). The remaining authors declare no conflict of interest. ACKNOWLEDGMENTS We thank Dr Celine Cansell, Ms Raffaella Chianese and the staff of the Medical Research Facility for technical assistance. We thank Dr Vladimir Orduña for the scientific advice and technical assistance.Peer reviewedPublisher PD

DNCs Require More Planning Steps

Many recent works use machine learning models to solve various complex algorithmic problems. However, these models attempt to reach a solution without considering the problem's required computational complexity, which can be detrimental to their ability to solve it correctly. In this work we investigate the effect of computational time and memory on generalization of implicit algorithmic solvers. To do so, we focus on the Differentiable Neural Computer (DNC), a general problem solver that also lets us reason directly about its usage of time and memory. In this work, we argue that the number of planning steps the model is allowed to take, which we call "planning budget", is a constraint that can cause the model to generalize poorly and hurt its ability to fully utilize its external memory. We evaluate our method on Graph Shortest Path, Convex Hull, Graph MinCut and Associative Recall, and show how the planning budget can drastically change the behavior of the learned algorithm, in terms of learned time complexity, training time, stability and generalization to inputs larger than those seen during training