Low levels of financial literacy cause people to have lower savings rates, higher transaction costs, larger debts and the loans acquisition with higher interest rates, therefore it becomes relevant to analyze the determinants of financial literacy. The aim of this research is to identify whether there is an association between the financial literacy level and sociodemographic characteristics. The Mexican Petroleum Company (Pemex) employees is the population analyzed. Pemex is the state-owned oil and natural gas producer, transporter, refiner and marketer in Mexico. A non-probabilistic convenience sampling was performed and 404 responses were obtained. The analysis of data was carried out with the Bayesian method. The results show that there is an association between Pemex employees’ level of financial literacy and their level of education, income, age and type of retirement saving. No association was found between their level of financial literacy and gender, marital status and whether or not they have children.

This study explores the experiences and perceptions of Chinese postgraduate students in the UK regarding online learning, focusing on the Community of Inquiry (CoI) framework. Semi-structured interviews were used to collect qualitative data, which were analyzed thematically. The findings reveal positive perceptions of online learning, challenges related to technology and infrastructure, the significance of social interaction and collaboration, and the limited impact of teaching quality on student satisfaction. The study emphasizes the importance of the CoI framework in designing effective online learning environments. Limitations include a small sample size and potential bias. Future research should involve larger and more diverse samples, investigate different teaching strategies, and enhance student agency and self-regulated learning in online education. Overall, this study contributes to understanding the applicability of the CoI framework and its potential for improving online learning experiences.

The concept of output-oriented education has been introduced for many years in our country and has been widely used in the process of personnel training in Chinese universities. This paper discusses how the concept of Outcome Based Education can be fully integrated into the process of developing talents in an interdisciplinary and collaborative manner in the context of new engineering. We have made useful explorations in various aspects from curriculum system integration, online teaching resources construction, studio-style course organization mode, rich teaching project library to school-enterprise cooperation project practice, etc., which have improved students' learning effect.

The number of accidents at level railway crossings, especially crossings without gate barriers/attendants, is still very high due to technical problems, driving culture, and human error. The aim of this research is to provide road maps application based on ergonomic visual displays design that can increase awareness level for drivers before crossing railway crossings. The double awareness driving (DAD) map information system was built based on the waterfall method, which has 4 steps: defining requirements, system and software design, unit testing, and implementation. User needs to include origin-destination location, geolocation, distance & travel time, directions, crossing information, and crossing notifications. The DAD map application was tested using a usability test to determine the ease of using the application used the System Usability Scale (SUS) questionnaire and an Electroencephalogram (EEG) test to determine the increase in concentration in drivers before and immediately crossing a railway crossing. Periodically, the application provides information on the driving zone being passed; green zone for driving distances > 500 m to the crossing, the yellow zone for distances 500m to 100m, and the red zone for distances < 100 m. The DAD map also provides information on the position and speed of the nearest train that will cross the railway crossing. The usability test for 10 respondents giving SUS score = 97.5 (satisfaction category) with a time-based efficiency value = 0.29 goals/s, error rate = 0%, and a success rate of 93.33%. The cognitive ergonomic testing via Electroencephalogram (EEG) produced a focus level of 21.66%. Based on the results of DAD map testing can be implemented to improve the safety of level railroad crossings in an effort to reduce the number of driving accidents.

Since January 1, 2016, the national population and family having two child policy comprehensively, the whole country gradually ushered in the second the arrival of the age of the child, in terms of my garden at this stage, the second child children have been accounted for dominated nearly 1/5 of the total number of children, for big kindergarten children, all of a sudden more than a new member in the home, he (she) to share the love of your family, Let the orderly life become no longer regular, their psychological more or less confused and anxious, this paper will from the perspective of kindergarten teachers in our kindergarten in the "two-child era" background to focus on the health of children's psychological practice and exploration.

The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant interest in modern agriculture. The appeal of AI arises from its ability to rapidly and precisely analyze extensive and complex information, allowing farmers and agricultural experts to quickly identify plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant attention in the world of agriculture and agronomy. By harnessing the power of AI to identify and diagnose plant diseases, it is expected that farmers and agricultural experts will have improved capabilities to tackle the challenges posed by these diseases. This will lead to increased effectiveness and efficiency, ultimately resulting in higher agricultural productivity and reduced losses caused by plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has resulted in significant benefits in the field of agriculture. By using AI technology, farmers and agricultural professionals can quickly and accurately identify illnesses affecting their crops. This allows for the prompt adoption of appropriate preventative and corrective actions, therefore reducing losses caused by plant diseases.