Cognitive Antecedents of the Agricultural Entrepreneurship Intentions of Indian University Students

The purpose of this chapter is to extend the research on determinants of entrepreneurial intentions in the agricultural industry by using the theoretical framework of determinants of entrepreneurial intentions and entrepreneurial event model. By employing the DEI and EEM, the researchers evaluate how perceived desirability, perceived feasibility, individual background, and triggering events can influence the attitude of an individual and in turn how entrepreneurial attitude can control entrepreneurial intentions. To achieve the objective, a questionnaire survey was held using the sample of 335 PhD, master, and bachelor students in commerce, business, and economics from an Indian central university. The data was analysed using a linear regression model. The findings advocate that perceived desirability, perceived feasibility, individual background, and triggering events are positively related to entrepreneurial attitude, and the entrepreneurial attitude positively and significantly influences entrepreneurial intentions in the agricultural sector.

How an election can be safely planned and conducted during a pandemic: Decision support based on a discrete event model

In 2020, the ongoing COVID-19 pandemic caused major limitations for any aspect of social life and in specific for all events that require a gathering of people. While most events of this kind can be postponed or cancelled, democratic elections are key elements of any democratic regime and should be upheld if at all possible. Consequently, proper planning is required to establish the highest possible level of safety to both voters and scrutineers. In this paper, we present the novel and innovative way how the municipal council and district council elections in Vienna were planned and conducted using an discrete event simulation model. Key target of this process was to avoid queues in front of polling stations to reduce the risk of related infection clusters. In cooperation with a hygiene expert, we defined necessary precautions that should be met during the election in order to avoid the spread of COVID-19. In a next step, a simulation model was established and parametrized and validated using data from previous elections. Furthermore, the planned conditions were simulated to see whether excessive queues in front of any polling stations could form, as these could on the one hand act as an infection herd, and on the other hand, turn voters away. Our simulation identified some polling stations where long queues could emerge. However, splitting up these electoral branches resulted in a smooth election across all of Vienna. Looking back, the election did not lead to a significant increase of COVID-19 incidences. Therefore, it can be concluded that careful planning led to a safe election, despite the pandemic.

The protein-protein interaction ontology: for better representing and capturing the biological context of protein interaction

Background With the rapid increase in the amount of Protein-Protein Interaction (PPI) data, the establishment of an event-centered PPI ontology that contains temporal and spatial vocabularies is urgently needed to clarify PPI biological annotations. In this paper, we propose a precisely designed schema - PPIO (PPI Ontology) for representing the biological context of PPIs. Results Inspired by the event model and the distinct characteristics of PPI events, PPIO consists of six core aspects of the information required for reporting a PPI event, including the interactor (who), the biological process (when), the subcellular location (where), the interaction type (how), the biological function (what) and the detection method (which). PPIO is implemented through the integration of appropriate terms from the corresponding vocabularies/ontologies, e.g., Gene Ontology, Protein Ontology, PSI-MI/MOD, etc. To assess PPIO, an approach based on PPIO in developed to extract PPI biological annotations from an open standard corpus “BioCreAtIvE-PPI”. The experiment results demonstrate PPIO’s high performance, a precision of 0.69, a recall of 0.72 and an F-score of 0.70. Conclusions PPIO is a well-constructed essential ontology in the interpretation of PPI biological context. The results of the experiments conducted on the BioCreAtIvE corpus demonstrate that PPIO is able to facilitate PPI annotation extraction from biomedical literature effectively and enrich essential annotation for PPIs.

Adaptive Design For Identifying Maximum Tolerated Dose Early To Accelerate Dose-Finding Trial

Purpose: The early identification of maximum tolerated dose (MTD) in phase I trial leads to faster progression to a phase II trial or an expansion cohort to confirm efficacy.Methods: We propose a novel adaptive design for identifying MTD early to accelerate dose-finding trials. The early identification of MTD is determined adaptively by dose-retainment probability using a trial data via Bayesian analysis. We applied the early identification design to an actual trial. A simulation study evaluates the performance of the early identification design.Results: In the actual study, we confirmed the MTD could be early identified and the study period was shortened. In the simulation study, the percentage of the correct MTD selection in the early identification Keyboard and early identification Bayesian optimal interval (BOIN) designs was almost same from the non-early identification version. The early identification Keyboard and BOIN designs reduced the study duration by about 50% from the model-assisted designs. In addition, the early identification Keyboard and BOIN designs reduced the study duration by about 20% from time-to-event model-assisted designs.Conclusion: We proposed the early identification of MTD maintaining the accuracy to be able to short the study period.

SIMULATION MODEL FOR RELIABILITY ESTIMATION OF NON-MAINTAINED RAILWAY TELECOMMUNICATION DEVICES

The paper presents a discrete-event model of non-maintained railway telecommunication equipment operation. The model developed in the AnyLogic environment allows to estimate the availability factor and specific quality indicator of such systems, as well as to predict time spent in various states. The presented model can be used both at the design and operation stage for the compliance assessment of telecommunication equipment parameters with the reliability requirements.