Arjomandfar, A., Campbell, C. M., Doostie, H., Semigroups related to certain group presentations, Semigroup Forum, Volume 85, Issue 3, (2012), 533-539.
 Ates, F., Cevik, A. S., The p-Cockcroft Property of Central Extensions of Groups II, Monatshefte fr Math., 150 (2007), 181-191.
 Ayık, H., Kuyucu, F., Vatansever, B., On Semigroup Presentations and Efficiency, Semigroup Forum, Vol. 65, (2002) 329335.
 Baik, Y.G., Pride, S.J. On the Efficiency of Coxeter Groups, Bull. of
Liu, L., Sha, M., and Park, H. (2013). Exploring the efficiency and utility of methods to recruit non-English speaking qualitative research participants. Survey Practice, 6(3), 1-8. Available at: http://www.surveypractice.org/index.php/SurveyPractice (accessed December 2013).
Markus, H.R., and Kitayama, S. (1991). Culture and the Self: Implications for Cognition, Emotion, and Motivation. Psychological Review, 20, 568-579.
Maxwell, A.E., Bastani, R., Vida, P., and Warda, S. (2005). Strategies to Recruit and Retain
Anonîms (2011). Latvijas lauksaimniecîba 2011 [Agriculture in Latvia 2011]. Latvijas Republikas Zemkopîbas ministrija. Retrieved 20 February 2013, from http://www.zm.gov.lv/doc_upl/LS_2012.pdf (in Latvian).
Baniuniene, A., Zekaite, V. (2008). The effect of mineral and organic fertilizers on potato tuber yield and quality. Latv. J. Agron. , 11 , 202-206.
Dalla Costa, L., Delle Vedove, G., Gianquinto, G., Giovanardi, R., Peressotti, A. (1997). Yield, water use efficiency and nitrogen uptake
1. Ariss, R. T. (2010). On the implications of market power in banking: Evidence from developing countries. Journal of banking & Finance , Vol. 34, pp. 765-775.
2. Berger, A. (1993). ’Distribution-Free’ Estimates of Efficiency in the U.S. Banking Industry and Tests of the Standard Distributional Assumptions. Journal of Productivity Analysis , Vol. 4, pp. 261-292.
3. Berger, A., Hannan T. (1998). The efficiency cost of market power in the banking industry: A test of the “quiet life” and related hypotheses, Review of Economics and
We propose linear parameter-varying (LPV) model-based approaches to the synthesis of robust fault detection and diagnosis (FDD) systems for loss of efficiency (LOE) faults of flight actuators. The proposed methods are applicable to several types of parametric (or multiplicative) LOE faults such as actuator disconnection, surface damage, actuator power loss or stall loads. For the detection of these parametric faults, advanced LPV-model detection techniques are proposed, which implicitly provide fault identification information. Fast detection of intermittent stall loads (seen as nuisances, rather than faults) is important in enhancing the performance of various fault detection schemes dealing with large input signals. For this case, a dedicated fast identification algorithm is devised. The developed FDD systems are tested on a nonlinear actuator model which is implemented in a full nonlinear aircraft simulation model. This enables the validation of the FDD system’s detection and identification characteristics under realistic conditions.
.: Geometry of E-optimality, Ann. Stat. 21 (1993), 416-433.
 FILOV´A, L.-TRNOVSK´A, M.-HARMAN, R.: Computing maximin efficient experimental designs using the methods of semidefinite programming, Metrika 75 (2012), 709-719.
 FILOV´A, L.-HARMAN, R.- KLEIN, T.: Approximate E-optimal designs for the model of spring balance weighing with a constant bias, J. Stat. Plann. Inference 141 (2011), 2480-2488.
 HARMAN, R.: Minimal efficiency of designs under the class of orthogonally invariant information criteria, Metrika 60
Marijana Zekić-Sušac, Rudolf Scitovski and Adela Has
. (2016). Soft computing methodologies for estimation of energy consumption in buildings with different envelope parameters. Energy Efficiency , Vol. 9, No. 2, pp. 435-453.
8. Patterson, M. G. (1996). What is energy efficiency?: Concepts, indicators and methodological issues. Energy Policy , Vol. 24, No. 5, pp. 377-390.
9. Prieto, A., Prieto, B., Martinez Ortigosa, E., Ros, E., Pelayo, F., Ortega, J., Rojas, I. (2016). Neural networks: An overview of early research, current frameworks and new challenges. Neurocomputing , Vol. 204, pp. 242-268.
of the Cabinet of Ministers of the Republic of Latvia (adopted 31 October 2006, with amendments). http://www.likumi.lv/doc.php?id=147522
Anonymous (2008). Health Care Efficiency Measures: Identification, Categorization, and Evaluation . The Agency for Healthcare Research and Quality of the U.S. Department of Health & Human Services. http://www.ahrq.gov/qual/efficiency/
Anonymous (2011). Composition and structure of consumption expenditure average per household member per month, 2010. Central Statistical Bureau of Latvia. http
Reinforcement learning (RL) constitutes an effective method of controlling dynamic systems without prior knowledge. One of the most important and difficult problems in RL is the improvement of data efficiency. Probabilistic inference for learning control (PILCO) is a state-of-the-art data-efficient framework that uses a Gaussian process to model dynamic systems. However, it only focuses on optimizing cumulative rewards and does not consider the accuracy of a dynamic model, which is an important factor for controller learning. To further improve the data efficiency of PILCO, we propose its active exploration version (AEPILCO) that utilizes information entropy to describe samples. In the policy evaluation stage, we incorporate an information entropy criterion into long-term sample prediction. Through the informative policy evaluation function, our algorithm obtains informative policy parameters in the policy improvement stage. Using the policy parameters in the actual execution produces an informative sample set; this is helpful in learning an accurate dynamic model. Thus, the AEPILCO algorithm improves data efficiency by learning an accurate dynamic model by actively selecting informative samples based on the information entropy criterion. We demonstrate the validity and efficiency of the proposed algorithm for several challenging controller problems involving a cart pole, a pendubot, a double pendulum, and a cart double pendulum. The AEPILCO algorithm can learn a controller using fewer trials compared to PILCO. This is verified through theoretical analysis and experimental results.
This paper considers main effects plans used to study m two-level factors using n runs which are partitioned into b blocks of equal size k = n/b. The assumptions are adopted that n ≡ 2 (mod 8) and k > 2 is even. Certain designs not having all main effects orthogonal to blocks were shown by Jacroux (2011a) to be D-optimal when (m − 2)(k − 2) + 2 ⩽ n ⩽ (m − 1)(k − 2) + 2. Here, we extend that result. For (m − 3)(k − 2) + 2 ⩽ n < (m − 2)(k − 2) + 2, the D-optimality of those designs is proved. Moreover, their D-efficiency is shown to be close to one for 2(m + 1) ⩽ n < (m − 3)(k − 2) + 2, indicating their good performance under the criterion of D-optimality.