Use Of B1 Research Articles

INTERFERENCE OF JAVANESE BOJONEGORO DIALECT IN INDONESIAN LANGUAGE LEARNING HIGH CLASS STUDENTS SDN 1 JIPO, KEPOHBARU, BOJONEGORO

Interference is one of the effects of language contact. Interference is interpreted as interference, interference, or the inclusion of language elements into other languages. This research is intended to find out the forms of interference and possible solutions to improve Indonesian language learning, especially for students at SDN 1 Jipo, Kepohbaru, Bojonegoro. This research is a type of descriptive qualitative research. The data in this study are transcripts of student conversations when learning Indonesian takes place. Data collection was carried out using observation, recording, interviewing, and listening techniques. Then do preduction and presentation of data, as well as drawing conclusions. The last step, the researchers conducted time triangulation, to experts, and discussions. Based on the results of data analysis,five forms of language interference were found, namely cultural interference, semantics, syntax, morphology and phonology. Cultural interference occurs due to students' lack of knowledge of the target language culture (B2). Semantic interference occurs in the use of words that have variables in a language. Syntactic interference, including the grammatical system of B1 mixed with B2. Morphological interference, occurs in the formation and change of word formsin the use of B1 due to friction with B2. Phonological interference, there are errors in pronunciation or pronunciation of sounds in Indonesian because they are influenced byregional language (Javanese).Language interference is difficult to eliminate so we need a way to minimize its impact. The methods that can be used are the teacher's efforts, student awareness, and the role of the environment.

Variation of performance and emission characteristics of a diesel engine fueled with diesel, rapeseed oil and hazelnut oil methyl ester blends

Abstract Turkey's economy is mainly dependent on agriculture and the country provides almost all petroleum demand through imports, the evaluation of vegetable alternative engine fuels is of great importance. In this study, usability of methyl ester of rapeseed oil and hazelnut produced abundant in Turkey was examined. Experiments were carried out in a four-cylinder, four-stroke, 46 kW, direct injection diesel engine. A comparison of diesel fuel, the rapeseed oil methyl ester and the hazelnut oil methyl ester blends was made from the engine performance and emissions point of view. Engine performance and emission tests were carried out with 4 different fuel that 100% diesel (SD), 50% rapeseed oil methyl ester and 50% diesel (B1), 50% hazelnut oil methyl ester and %50 diesel (B2), 25% rapeseed oil methyl ester, 25% rapeseed oil methyl ester and 50% diesel (B3). Highest engine performance and lowest specific fuel consumption were obtained with SD fuel. But the use of biodiesel led to reduction in CO and smoke emissions accompanying with the imperceptible torque loss. As the rapeseed methyl ester rate increased in the blend, smoke and CO emissions decreased, NOx and CO2 emissions increased. With the use of B1 fuel, NOx emissions increased up to 7.2%.

Approaches to handling pharmacodynamic baseline responses

A few approaches for handling baseline responses are available for use in pharmacokinetic (PK)-pharmacodynamic (PD) analysis. They include: (method 1-B1) estimation of the typical value and interindividual variability (IIV) of baseline in the population, (B2) inclusion of the observed baseline response as a covariate acknowledging the residual variability, (B3) a more general version of B2 as it also takes the IIV of the baseline in the population into account, and (B4) normalization of all observations by the baseline value. The aim of this study was to investigate the relative performance of B1-B4. PD responses over a single dosing interval were simulated from an indirect response model in which a drug acts through stimulation or inhibition of the response according to an Emax model. The performance of B1-B4 was investigated under 22 designs, each containing 100 datasets. NONMEM VI beta was used to estimate model parameters with the FO and the FOCE method. The mean error (ME, %) and root mean squared error (RMSE, %) of the population parameter estimates were computed and used as an indicator of bias and imprecision. Absolute ME (|ME|) and RMSE from all methods were ranked within the same design, the lower the rank value the better method performance. Average rank of each method from all designs was reported. The results showed that with B1 and FOCE, the average of |ME| and RMSE across all typical individual parameters and all conditions was 5.9 and 31.8%. The average rank of |ME| for B1, B2, B3, and B4 was 3.7, 3.8, 3.3, and 5.2 for the FOCE method, and 4.6, 4.3, 4.7, and 6.4 for the FO method. The smallest imprecision was noted with the use of B1 (rank of 3.1 for FO, and 2.9 for FOCE) and increased, in order, with B3 (3.9-FO and 3.6-FOCE), B2 (4.8-FO; 4.7-FOCE), and B4 (6.4-FO; 6.5-FOCE). We conclude that when considering both bias and imprecision B1 was slightly better than B3 which in turn was better than B2. Differences between these methods were small. B4 was clearly inferior. The FOCE method led to a smaller bias, but no marked reduction in imprecision of parameter estimates compared to the FO method.