Slicing AADL Specifications for Model Checking

To combat the state-space explosion problem in model checking larger systems, abstraction techniques can be employed. Here, methods that operate on the system specification before constructing its state space are preferable to those that try to minimize the resulting transition system as they generally reduce peak memory requirements. We sketch a slicing algorithm for system specifications written in (a variant of) the Architecture Analysis and Design Language (AADL). Given a specification and a property to be verified, it automatically removes those parts of the specification that are irrelevant for model checking the property, thus reducing the size of the corresponding transition system. The applicability and effectiveness of our approach is demonstrated by analyzing the state-space reduction for an example, employing a translator from AADL to Promela, the input language of the SPIN model checker

ESTIMATION OF VARIABILITY ANALYSIS PARAMETERS FOR MAJOR GROWTH AND FLOWERING TRAITS OF Lilium leichtlinii var. maximowiczii GERMPLASM

This experiment was carried out to evaluate the Lilium leichtlinii var maximowiczii germplasm collected from the different natural habitats from all over Korea. In total 30 accessions were studied for nine traits viz. plant height, leaf length, leaf width, the numbers of flowers, flower diameter, length of outer tepal, width of outer tepal, the number of leaf burn, and days to flowering in randomized block design with three replications. The ANOVA revealed highly significant variability prevailing among the investigated genotypes for almost all studied traits (except leaf width). The higher estimated value of GCV, PCV, heritability (H2), and genetic advance as percent of the mean was obtained for the number of flowers and leaf burn. The moderate to high GCV and PCV coupled with higher heritability estimates (H2) and GAM were found for plant height and flower diameter. Progeny selection would be effective as the prevalence of additive gene effect for these traits. Besides, leaf width, leaf length, length of outer tepal, the width of outer tepal, and days to flowering traits were possessed moderate to low GCV and PCV value coupled with the moderate value of heritability estimate with the low level of GAM proved to be the prevalence of non-additive genetic effect thereby indicating the necessity of alternative breeding approach for these traits improvement and breeding scheme. For the former group of traits breeding hybridization and selection would be an effective method, and primarily mean performance of these traits would be very handy for the decision of selection

Dairy Value Chain In Vietnam: Evidences from Bavi Area

Dairy farming, in Vietnam, existed in the early twentieth century thanks to the favorable natural advantage. During many difficult periods, the Vietnam’s dairy industry has developed constantly and contributed significantly to the food needs ensuring. However, Vietnam’s dairy industry still could not satisfy the domestic milk demand. Retail milk prices in Vietnam are very high, whereas the price of milk sold by the dairy farmers is very low. The cause stems from the control of dairy companies in the quantity and quality of milk. Moreover, that control caused an imbalance in the profits and benefits of each actor in the dairy value chain. This study, hence, finds out the distribution of benefits, costs, value-added among the actors, and problems in the practical management in dairy milk value chain with specific focus on Bavi as the case study

NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models

This paper describes the NOWJ1 Team's approach for the Automated Legal Question Answering Competition (ALQAC) 2023, which focuses on enhancing legal task performance by integrating classical statistical models and Pre-trained Language Models (PLMs). For the document retrieval task, we implement a pre-processing step to overcome input limitations and apply learning-to-rank methods to consolidate features from various models. The question-answering task is split into two sub-tasks: sentence classification and answer extraction. We incorporate state-of-the-art models to develop distinct systems for each sub-task, utilizing both classic statistical models and pre-trained Language Models. Experimental results demonstrate the promising potential of our proposed methodology in the competition.Comment: ISAILD@KSE 202