52 research outputs found
Nutrient digestion by dairy cows fed diets replacing starch with non-forage fiber.
No full textCorn starch is used as the main energy source in lactating dairy cow diets. Feeding high levels of corn starch may be associated with negative health impacts on lactating dairy cows, such as ruminal acidosis and laminitis along with higher feed costs and lower income from reduced milk components. Dried distillers grains with solubles (DG), a co-product of the ethanol industry, is an excellent source of energy. Ranathunga et al. (2010) demonstrated that that incrementally reducing the amount of starch in a ration from a high of 29% to a low of 20% by adding DG resulted in similar milk production and composition by lactating dairy cows. The objective of the study was to evaluate the effect of replacing starch from corn with non-forage fiber from DG and soybean hulls on the nutrient flow to the omasum, ruminal nutrient degradability, total tract nutrient digestibility, and nitrogen partition of lactating dairy cows
A Study on the impact of employee perception on the success of IT startups
No full textSetting up and running new IT based businesses become more challenging and frustrating for entrepreneurs, investors and employees because of the instability of internal and external environments. Not like well- established organizations, usually the stakeholders of startups share a different state of risk factors among them. This study demonstrates how the perception of employees impacts on success of startups. Eight constructs (Confidence and trust in the owner or partners (CTOP), Confidence and trust in the organisation (CTO), Interest in the employees’ future (IEF), Fare remuneration and benefits (FRB), Actively seeking employees’ ideas and opinions (ASIO), Communicating information and needs in the organisation (CINO), Train employees to solve problems (TESP), Recognising employees’ involvement and accomplishments (REIA) ) have been used to derive the perceived value of employees. Four constructs (Growth, Profitability, Investment on R&D, Customers Satisfaction) are used to measure the success of IT startups.
The main hypothesis of this model is proven in this research study in which perception of employees is positively related to success of IT startups. Some factors such that Confidence and trust in the owner or partners (CTOP), Confidence and trust in the organisation (CTO), Interest in the employees’ future (IEF) and Interest in the employees’ future (IEF) have respectively higher correlation to the perception of employees in IT startups. Similarly profitability (PRO) and growth (GR) indicates a higher correlation to determine the success of startups.
The main constructs identified in this research has correlated to its related constructs while below attributes has been statistically proven that has no correlation to its related constructs these are; CINO_AI (Access to the org Information), FRB_IB (Incentives/ Bonuses), CTOP_DE (Determination of owner), CTOP_E (Experience of owner), CTOP_SS (Social skills-networking with the targeted audience), SIO_JI (Job involvement) and CTOP_SK(Skill and knowledge of owne
Interfacing a cognitive agent platform with Second Life
No full textSecond Life is a multi-purpose online virtual world that provides a rich platform for remote human interaction. It is increasingly being used as a simulation platform to model complex human interactions in diverse areas, as well as to simulate multi-agent systems. It would therefore be beneficial to provide techniques allowing high-level agent development tools, especially cognitive agent platforms such as belief-desire-intention (BDI) programming frameworks, to be interfaced to Second Life. This is not a trivial task as it involves mapping potentially unreliable sensor readings from complex Second Life simulations to a domain-specific abstract logical model of observed properties and/or events. This paper investigates this problem in the context of agent interactions in a multi-agent system simulated in Second Life. We present a framework which facilitates the connection of any multi-agent platform with Second Life, and demonstrate it in conjunction with an extension of the Jason BDI interpreter.Unpublished1. Linden Lab. Second Life Home Page. http://secondlife.com
2. OpenMetaverse Organization. libopenmetaverse developer wiki. http://lib.openmetaverse.org/wiki/Main_Page
3. Ranathunga, S., Cranefield, S., Purvis, M.: Integrating Expectation Handling into Jason. Discussion Paper 2011/03, Department of Information Science, University of Otago (2011). http://eprints.otago.ac.nz/1093/
4. Cranefield, S., Winikoff, M.: Verifying social expectations by model checking truncated paths. Journal of Logic and Computation (2010). Advance access, doi:10.1093/logcom/exq055
5. Veksler, V.D.: Second Life as a Simulation Environment: Rich, high-fidelity world, minus the hassles. In: Proceedings of the 9th International Conference of Cognitive Modeling (2009)
6. Weitnauer, E., Thomas, N., Rabe, F., Kopp, S.: Intelligent agents living in social virtual environments bringing Max into Second Life. In: H. Prendinger, J. Lester, M. Ishizuka (eds.) Intelligent Virtual Agents, Lecture Notes in Computer Science, vol. 5208, pp. 552–553. Springer Berlin / Heidelberg (2008)
7. Bordini, R.H., Hubner, J.F., Wooldridge, M.: Programming Multi-Agent Systems in AgentSpeak using Jason. John Wiley & Sons Ltd, England (2007)
8. EsperTech. Esper Tutorial. http://esper.codehaus.org/tutorials/tutorial/tutorial.html
9. Vstex Company. SecondFootball Home Page. http://www.secondfootball.com
10. Varvello, M., Picconi, F., Diot, C., Biersack, E.: Is there life in Second Life? In: Proceedings of the 2008 ACM CoNEXT Conference, CoNEXT ’08, pp. 1:1–1:12. ACM, New York, NY, USA (2008)
11. Eno, J., Gauch, S., Thompson, C.: Intelligent crawling in virtual worlds. In: Pro- ceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03, WI-IAT ’09, pp. 555–558. IEEE Computer Society, Washington, DC, USA (2009)
12. Kappe, F., Zaka, B., Steurer, M.: Automatically detecting points of interest and social networks from tracking positions of avatars in a virtual world. In: Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, pp. 89–94. IEEE Computer Society, Washington, DC, USA (2009)
13. Friedman, D., Steed, A., Slater, M.: Spatial social behavior in Second Life. In: C. Pelachaud, J.C. Martin, E. Andr, G. Chollet, K. Karpouzis, D. Pel (eds.) Intelligent Virtual Agents, Lecture Notes in Computer Science, vol. 4722, pp. 252–263. Springer Berlin / Heidelberg (2007)
14. Yee, N., Bailenson, J.N., D, P., Urbanek, M., Chang, F., Merget, D.: The unbearable likeness of being digital; the persistence of nonverbal social norms in online virtual environments. Cyberpsychology and Behavior 10, 115–121 (2007)
15. La, C.A., Michiardi, P.: Characterizing user mobility in Second Life. In: Proceedings of the first workshop on Online social networks, WOSP ’08, pp. 79–84. ACM, New York, NY, USA (2008)
16. Cranefield, S., Li, G.: Monitoring social expectations in Second Life. In: J. Padget, A. Artikis, W. Vasconcelos, K. Stathis, V. Silva, E. Matson, A. Polleres (eds.) Coordination, Organizations, Institutions and Norms in Agent Systems V, Lecture Notes in Artificial Intelligence, vol. 6069, pp. 133–146. Springer (2010)
17. Burden, D.J.H.: Deploying embodied AI into virtual worlds. Knowledge-Based Systems 22, 540–544 (2009)
18. Ullrich, S., Bruegmann, K., Prendinger, H., Ishizuka, M.: Extending MPML3D to Second Life. In: H. Prendinger, J. Lester, M. Ishizuka (eds.) Intelligent Virtual Agents, Lecture Notes in Computer Science, vol. 5208, pp. 281–288. Springer Berlin / Heidelberg (2008)
19. Jan, D., Roque, A., Leuski, A., Morie, J., Traum, D.: A virtual tour guide for virtual worlds. In: Proceedings of the 9th International Conference on Intelligent Virtual Agents, IVA ’09, pp. 372–378. Springer-Verlag, Berlin, Heidelberg (2009)
20. Bogdanovych, A., Rodriguez-Aguilar, J.A., Simoff, S., Cohen, A.: Authentic interactive reenactment of cultural heritage with 3D virtual worlds and artificial intelligence. Applied Artificial Intelligence 24(6), 617–647 (2010
Interfacing a cognitive agent platform with Second Life
No full textSecond Life is a multi-purpose online virtual world that provides a rich platform for remote human interaction. It is increasingly being used as a simulation platform to model complex human interactions in diverse areas, as well as to simulate multi-agent systems. It would therefore be beneficial to provide techniques allowing high-level agent development tools, especially cognitive agent platforms such as belief-desire-intention (BDI) programming frameworks, to be interfaced to Second Life. This is not a trivial task as it involves mapping potentially unreliable sensor readings from complex Second Life simulations to a domain-specific abstract logical model of observed properties and/or events. This paper investigates this problem in the context of agent interactions in a multi-agent system simulated in Second Life. We present a framework which facilitates the connection of any multi-agent platform with Second Life, and demonstrate it in conjunction with an extension of the Jason BDI interpreter.Unpublished1. Linden Lab. Second Life Home Page. http://secondlife.com
2. OpenMetaverse Organization. libopenmetaverse developer wiki. http://lib.openmetaverse.org/wiki/Main_Page
3. Ranathunga, S., Cranefield, S., Purvis, M.: Integrating Expectation Handling into Jason. Discussion Paper 2011/03, Department of Information Science, University of Otago (2011). http://eprints.otago.ac.nz/1093/
4. Cranefield, S., Winikoff, M.: Verifying social expectations by model checking truncated paths. Journal of Logic and Computation (2010). Advance access, doi:10.1093/logcom/exq055
5. Veksler, V.D.: Second Life as a Simulation Environment: Rich, high-fidelity world, minus the hassles. In: Proceedings of the 9th International Conference of Cognitive Modeling (2009)
6. Weitnauer, E., Thomas, N., Rabe, F., Kopp, S.: Intelligent agents living in social virtual environments bringing Max into Second Life. In: H. Prendinger, J. Lester, M. Ishizuka (eds.) Intelligent Virtual Agents, Lecture Notes in Computer Science, vol. 5208, pp. 552–553. Springer Berlin / Heidelberg (2008)
7. Bordini, R.H., Hubner, J.F., Wooldridge, M.: Programming Multi-Agent Systems in AgentSpeak using Jason. John Wiley & Sons Ltd, England (2007)
8. EsperTech. Esper Tutorial. http://esper.codehaus.org/tutorials/tutorial/tutorial.html
9. Vstex Company. SecondFootball Home Page. http://www.secondfootball.com
10. Varvello, M., Picconi, F., Diot, C., Biersack, E.: Is there life in Second Life? In: Proceedings of the 2008 ACM CoNEXT Conference, CoNEXT ’08, pp. 1:1–1:12. ACM, New York, NY, USA (2008)
11. Eno, J., Gauch, S., Thompson, C.: Intelligent crawling in virtual worlds. In: Pro- ceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03, WI-IAT ’09, pp. 555–558. IEEE Computer Society, Washington, DC, USA (2009)
12. Kappe, F., Zaka, B., Steurer, M.: Automatically detecting points of interest and social networks from tracking positions of avatars in a virtual world. In: Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, pp. 89–94. IEEE Computer Society, Washington, DC, USA (2009)
13. Friedman, D., Steed, A., Slater, M.: Spatial social behavior in Second Life. In: C. Pelachaud, J.C. Martin, E. Andr, G. Chollet, K. Karpouzis, D. Pel (eds.) Intelligent Virtual Agents, Lecture Notes in Computer Science, vol. 4722, pp. 252–263. Springer Berlin / Heidelberg (2007)
14. Yee, N., Bailenson, J.N., D, P., Urbanek, M., Chang, F., Merget, D.: The unbearable likeness of being digital; the persistence of nonverbal social norms in online virtual environments. Cyberpsychology and Behavior 10, 115–121 (2007)
15. La, C.A., Michiardi, P.: Characterizing user mobility in Second Life. In: Proceedings of the first workshop on Online social networks, WOSP ’08, pp. 79–84. ACM, New York, NY, USA (2008)
16. Cranefield, S., Li, G.: Monitoring social expectations in Second Life. In: J. Padget, A. Artikis, W. Vasconcelos, K. Stathis, V. Silva, E. Matson, A. Polleres (eds.) Coordination, Organizations, Institutions and Norms in Agent Systems V, Lecture Notes in Artificial Intelligence, vol. 6069, pp. 133–146. Springer (2010)
17. Burden, D.J.H.: Deploying embodied AI into virtual worlds. Knowledge-Based Systems 22, 540–544 (2009)
18. Ullrich, S., Bruegmann, K., Prendinger, H., Ishizuka, M.: Extending MPML3D to Second Life. In: H. Prendinger, J. Lester, M. Ishizuka (eds.) Intelligent Virtual Agents, Lecture Notes in Computer Science, vol. 5208, pp. 281–288. Springer Berlin / Heidelberg (2008)
19. Jan, D., Roque, A., Leuski, A., Morie, J., Traum, D.: A virtual tour guide for virtual worlds. In: Proceedings of the 9th International Conference on Intelligent Virtual Agents, IVA ’09, pp. 372–378. Springer-Verlag, Berlin, Heidelberg (2009)
20. Bogdanovych, A., Rodriguez-Aguilar, J.A., Simoff, S., Cohen, A.: Authentic interactive reenactment of cultural heritage with 3D virtual worlds and artificial intelligence. Applied Artificial Intelligence 24(6), 617–647 (2010
Dried distillers grains with solubles as a non-forage fiber source in lactating dairy cow diets.
No full textDried distillers grains with solubles (DDGS) is a co-product of ethanol industry and traditionally fed as an alternative to soybean meal and corn. It has been recognized as an excellent source of energy, attributed to having high concentrations of digestible neutral detergent fiber (NDF) and fat. The dairy industry utilizes approximately 40 to 45% of DDGS produced in the United States. However, still there are challenges to overcome when feeding DDGS to lactating dairy cows. Greater concentrations of highly degradable non-forage fiber and polyunsaturated fatty acids and less physically effective non-forage fiber are considered as the negative qualities associated with DDGS. Five studies were conducted to overcome challenges of feeding DDGS to lactating dairy cows. The overall objectives of the all studies were to increase the efficiency of utilizing DDGS as a feed ingredient in lactating dairy cow diets and decrease the feed cost. The first study evaluated the effect of replacing starch from corn with non-forage fiber from DDGS and soybean hulls on nutrient flow to the omasum, ruminal nutrient digestibility, total tract nutrient digestibility, and nitrogen partitioning of lactating dairy cows. Results from the study suggested that when lactating dairy cows are fed DDGS to replace starch from corn, they derive energy through digesting non-forage fiber and crude fat of DDGS. The second study evaluated the effect of concentrations of forages and DDGS on production performance of lactating dairy cows. Results suggested that when lactating dairy cows were fed DDGS at 18% on DM basis with adequate forage fiber (\u3e21%), cows had greater milk production without having any adverse effects such as milk fat depression. The third study evaluated the effect of concentrations of forages and DDGS on ruminal fermentation and nutrient digestion in lactating dairy cows. This study demonstrated that DDGS had different degradation patterns and rate of passages with low and high forage concentrations. The fourth study evaluated the effects of concentrations of forages and DDGS on in situ degradability of DDGS. This study demonstrated that non-forage fiber of DDGS had less degradability with low forage diets whereas it had greater non-forage fiber degradability with high forage diets. The fifth study was conducted to evaluate the effect of DDGS on the fatty acid composition of rumen digesta and milk when fed with different forage concentrations. This study demonstrated that forage and DDGS concentrations in the diet change the fatty acid composition of rumen digesta and milk. Variations in the trans fatty acids in the milk and rumen digesta were not sufficient to explain the variations observed with milk fat concentration and yield. Finally, it was concluded that DDGS is not an effective fiber source to maintain milk fat concentration. But feeding DDGS at 18% of DM with sufficient forage fiber to lactating dairy cows maintain healthy rumen conditions and greater milk production without having any adverse effects such as milk fat depression at a lower feed cost
Dried distillers grains with solubles as a non-forage fiber source in lactating dairy cows.
No full textA comparison of methods to analyze physical effective factor and physically effective NDF in TMR and orts.
No full textEffects of different forage and dried distillers grains with solubles concentrations on sorting behavior of lactating dairy cows.
No full textIntegrating expectation monitoring into Jason: A case study using Second Life
No full textThis is the final version of a paper that was peer reviewed and accepted for presentation at the 8th European Workshop on Multi-Agent Systems, 2010 (which has no formal proceedings).Previous work on detecting fulfilments and violations of expectations (which may correspond to conventions, norms, commitments or contracts) assumed that information about the world is available as an abstract logical model of observed properties and/or events. There has been little investigation of practical techniques for mapping from sensor readings in a complex environment to such a logical model suitable for monitoring techniques. Moreover, there has been little work on investigating practical techniques for agents to respond to fulfilments and violations of their expectations. This paper investigates these two aspects in the context of interactions involving multiple Jason agents in a Second Life simulation. We present a framework that can be used to connect any agent platform with Second Life, and demonstrate how this framework is integrated with the Jason platform to monitor expectations of agents.PublishedPeer Reviewed1. Cranefield, S., Winikoff, M.: Verifying social expectations by model checking truncated paths. Journal of Logic and Computation (2010). Advance access, doi:10.1093/logcom/exq055
2. Spoletini, P., Verdicchio, M.: An automata-based monitoring technique for commitment-based multi-agent systems. In: Coordination, Organizations, Institutions and Norms in Agent Systems IV, Lecture Notes in Computer Science, vol. 5428, pp. 172–187. Springer (2009)
3. Second Life: http://secondlife.com/
4. Bogdanovych, A., Simoff, S., Esteva, M.: Virtual institutions: Normative environments facilitating imitation learning in virtual agents. In: Intelligent Virtual Agents, Lecture Notes in Artificial Intelligence, vol. 5208, pp. 456–464. Springer (2008)
5. Weitnauer, E., Thomas, N.M., Rabe, F., Kopp, S.: Intelligent agents living in social virtual environments – bringing Max into Second Life. In: Intelligent Virtual Agents, Lecture Notes in Artificial Intelligence, vol. 5208, pp. 552–553. Springer (2008)
6. Veksler, V.D.: Second Life as a simulation environment: Rich, high-fidelity world, minus the hassles. In: Proceedings of the 9th International Conference on Cognitive Modeling (2009)
7. Ullrich, S., Bruegmann, K., Prendinger, H., Ishizuka, M.: Extending MPML3D to Second Life. In: Intelligent Virtual Agents, Lecture Notes in Artificial Intelligence, vol. 5208, pp. 281–288. Springer (2008)
8. OpenMetaverse Foundation: http://www.openmetaverse.org/projects/libopenmetaverse
9. Vstex Company: Second Life football system. http://www.secondfootball.com/
10. Bordini, R., Hübner, J., M., W.: Programming multi-agent systems in AgentSpeak using Jason. John Wiley & Sons (2007)
11. Rao, A.S.: AgentSpeak(L): BDI agents speak out in a logical computable language. In: Agents Breaking Away: 7th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, Lecture Notes in Artificial Intelligence, vol. 1038, pp. 42–55. Springer (1996)
12. Bacchus, F., Kabanza, F.: Using temporal logics to express search control knowledge for planning. Artificial Intelligence 116(1-2), 123–191 (2000)
13. Dignum, F., Morley, D., Sonenberg, E.A., Cavedon, L.: Towards socially sophisticated BDI agents. In: Proceedings of the Fourth International Conference on MultiAgent Systems, pp. 111–118. IEEE Computer Society (2000)
14. Meneguzzi, F., Luck, M.: Norm-based behaviour modification in BDI agents. In: Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems, pp. 177–184. IFAAMAS (2009)
15. Neto, B.F.S., Silva, V.T., Lucena, C.J.P.: Using Jason to develop normative agents. In: Advances in Artificial Intelligence – SBIA 2010, Lecture Notes in Artificial Intelligence, vol. 6404, pp. 143–152. Springer (2011)
16. Meneguzzi, F., Miles, S., Luck, M., Holt, C., Smith, M.: Electronic contracting in aircraft aftercare: a case study. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 63–70. IFAAMAS (2008
No effect of monthly supplementation with 12000 IU, 24000 IU or 48000 IU vitamin D3 for one year on muscle function: The vitamin D in older people study
No full textVitamin D plays a role in muscle function through genomic and non-genomic processes. The objective of this RCT was to determine the effect of monthly supplemental vitamin D3onmuscle function in 70+ years old adults. Participants (n = 379) were randomized to receive, 12,000 IU, 24,000 IU or 48,000 IU of vitamin D3 monthly for 12 months. Standardized Hand Grip Strength (GS) and Timed-Up and Go (TUG) were measured before and after vitamin D3 supplementation. Fasting total plasma 25 hydroxyvitamin D (25OHD) and Parathyroid Hormone (PTH) concentrations were measured by Liquid Chromatography Tandem Mass Spectrometry (LC-MSMS) and immunoassay, respectively. Baseline plasma 25OHD concentrations were 41.3 (SD 19.9), 39.5 (SD 20.6), 38.9 (SD 19.7) nmol/L; GS values were 28.5 (SD 13.4), 28.8 (SD 13.0) and 28.1 (SD 12.1) kg and TUG test values were 10.8 (SD 2.5), 11.6 (SD 2.9) and 11.9 (SD 3.6) s for the 12,000 IU, 24,000 IU and 48,000 IU dose groups, respectively. Baseline plasma 25OHD concentration < 25 nmol/L was associated with lower GS (P = 0.003). Post-interventional plasma 25OHD concentrations increased to 55.9 (SD 15.6), 64.6 (SD15.3) and 79.0 (SD 15.1) nmol/L in the 12,000 IU, 24,000 IU and 48,000 IU dose groups, respectively and there was a significant dose-related response in post-interventional plasma 25OHD concentration (p<0.0001). Post-interventional GS values were 24.1 (SD 10.1), 26.2 (SD10.6) and 25.7 (SD 9.4) kg and TUG test values were 11.5 (SD 2.6), 12.0 (SD 3.7) and 11.9 (SD 3.2) s for 12,000 IU, 24,000 IU and 48,000 IU dose groups, respectively. The change (Δ) in GS and TUG from pre to post-intervention was not different between treatment groups before and after the adjustment for confounders, suggesting no effect of the intervention. Plasma 25OHD concentration was not associated with GS and TUG test after supplementation. In conclusion, plasma 25OHD concentration < 25 nmol/L was associated with lower GS at baseline. However, monthly vitamin D3 supplementation with 12,000 IU, 24,000 IU and 48,000 IU, for 12 months had no effect on muscle function in older adults aged 70+ years
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