Academic literature on the topic 'Identity Testing in Number Fields'

The current-season spread of Potato virus Y (PVY) was monitored in 19 fields under various management practices in New Brunswick, Canada, through the 2011 and 2012 growing seasons. The focus of this study was to evaluate the role of seedborne PVY inoculum, aphid vector abundance, and the numbers, timing, and types of insecticide and mineral oil sprays, and to confirm the reliability and forecasting capacity of midseason PVY testing. In each field, 100 to 110 virus-free plants were identified shortly after emergence and were assessed four times from early July to early September (after top-kill) with enzyme-linked immunosorbent assay (ELISA) and reverse-transcription polymerase chain reaction (RT-PCR) to track PVY spread. In addition, tubers harvested during development in August and after top-kill were grown-out in the greenhouse for ELISA testing. PVY spread to selected virus-free plants varied widely, ranging from 0 to 76.2% across all studied fields. Of the 19 fields over two seasons, 10 fields were planted with no detectable seedborne PVY, and they showed 0 to 8.7% (mean 2.9%) PVY spread by harvest. The remaining nine study fields with 0.9 to 5.8% seedborne PVY showed 1 to 76.2% (mean 15.2%) PVY spread by harvest. PVY spread was detected in most fields during midseason testing with ELISA and RT-PCR; all tests correlated well with final PVY rates after top-kill, though RT-PCR detection in developing tubers was most sensitive and correlated. Logistic regression modeling was used to identify major factors in PVY spread, including seedborne PVY, early-season aphid abundance, and the numbers of insecticide and mineral oil sprays. The best-fitting model, constructed using these factors as well as a measurement of July PVY incidence (ELISAJuly), strongly explained PVY spread by harvest, with the most significant management factor being the number of mineral oil sprays supplemented with insecticide used during the growing season. A similar model fitted without the ELISAJuly did not adequately predict ultimate PVY spread. The analysis suggests that mineral oil alone was effective at lowering PVY spread, and more effective when combined with insecticide, particularly when used early in the season. No evidence was found for differences in PVY spread across the eight cultivars used or across the range of mineral oil application rates, whereas some evidence was found for differences in the effectiveness of different insecticide types.

The rice fields in Bonto Rannu Village, Kajang District, Bulukumba Regency are dominated by rainfed rice fields. This species has limitations in the dry season because irrigation/agricultural irrigation completely depends on rainwater. Therefore, it is necessary to identify alternative irrigation sources to sustain farmers' farming activities in Bonto Rannu Village. This study aims to analyze the number of groundwater wells by testing in the form of a pump test (pumping test) with the step draw-down method. The optimum discharge (Qopt) of well 1 is 0.01876 m3/s or 67.53 m3/hour with an optimum Sw of 2.891 m. This shows that groundwater in well one can be pumped at 67.53 m3/hour with a decreased water level of 2.891 m, with the pump condition still technically safe. As for well 2, the optimum discharge (Qopt) is 0.00318 m3/s or 11.45 m3/hour with an optimum Sw of 2.100 m. These data indicate that there are limited groundwater reserves.

The article presents the results of approbation of the intellectual system for testing general language competences (ISTOK) developed by testologists, linguists, specialists in methodology of teaching foreign languages and in artificial intelligence. This system includes a range of tests assessing language ability at levels from A2 to C1 of the Common European Framework of Reference (CEFR), as well as an adaptive placement test. All test materials are calibrated according to the CEFR. ISTOK is an adaptive testing system deployed through neural network solutions and providing assessment of receptive and productive language skills (reading, listening, speaking and writing) by using artificial intelligence and/or neurolinguistic models. The process of ISTOK development implied, apart from writing test items, putting together databases of writing and speaking assignments marked by professional assessors and assessment criteria for productive skills, as well as algorithms to identify various types of mistakes with the help of artificial intelligence. The results of various testing cohorts with the total number of test takers exceeding 5,000 demonstrated high reliability and objectified test validity. The new approach to language skills testing can be used for various purposes in higher education institutions, as well and to identify and/or confirm language proficiency of personnel in different organisations and businesses, while the principles of training and practical use of neurolinguistic models will find wide application in various fields of applied research.

The purpose of the article is to study and search for optimal strategies for modelling information systems using pseudo-random numbers to improve the performance of information systems and to determine the optimal parameters of random number distributions; to analyze efficient algorithms for generating pseudo-random numbers. The research methods are based on the application of a systematic approach to analyzing the benefits of using pseudorandom numbers in the design of information systems. Scientific novelty. The article analyses the advantages of stochastic modelling of information systems using different approaches to generating pseudorandom numbers, and for the first time considers the application of a random decision tree forest for modelling queuing systems. Conclusions. The application of pseudorandom numbers in system design is an urgent problem that requires further research and development of methods. The design of efficient algorithms for generating, validating, and using pseudorandom values plays an essential role in many fields of science and technology. This approach allows for optimization and planning in the process of designing the system and is essential for validating and testing the system. By generating random variables, you can create different test scenarios and check how the system reacts to various conditions, which helps to identify potential errors, flaws, and problems in the system and ensures the quality of the design. Thus, the use of pseudo-random numbers in system design allows for randomness, risk analysis, optimization and planning, validation, and testing. This is an important aspect of system development that helps to ensure their reliability, performance, and efficiency.