Contribution of the MERISE-Type Conceptual Data Model to the Construction of Monitoring and Evaluation Indicators of the Effectiveness of Training in Relation to the Needs of the Labor Market in the Republic of Congo

This study proposes the use of the MERISE conceptual data model to create indicators for monitoring and evaluating the effectiveness of vocational training in the Republic of Congo. The importance of MERISE for structuring and analyzing data is underlined, as it enables the measurement of the adequacy between training and the needs of the labor market. The innovation of the study lies in the adaptation of the MERISE model to the local context, the development of innovative indicators, and the integration of a participatory approach including all relevant stakeholders. Contextual adaptation and local innovation: The study suggests adapting MERISE to the specific context of the Republic of Congo, considering the local particularities of the labor market. Development of innovative indicators and new measurement tools: It proposes creating indicators to assess skills matching and employer satisfaction, which are crucial for evaluating the effectiveness of vocational training. Participatory approach and inclusion of stakeholders: The study emphasizes actively involving training centers, employers, and recruitment agencies in the evaluation process. This participatory approach ensures that the perspectives of all stakeholders are considered, leading to more relevant and practical outcomes. Using the MERISE model allows for: • Rigorous data structuring, organization, and standardization: Clearly defining entities and relationships facilitates data organization and standardization, crucial for effective data analysis. • Facilitation of monitoring, analysis, and relevant indicators: Developing both quantitative and qualitative indicators helps measure the effectiveness of training in relation to the labor market, allowing for a comprehensive evaluation. • Improved communication and common language: By providing a common language for different stakeholders, MERISE enhances communication and collaboration, ensuring that all parties have a shared understanding. The study’s approach and contribution to existing research lie in: • Structured theoretical and practical framework and holistic approach: The study offers a structured framework for data collection and analysis, covering both quantitative and qualitative aspects, thus providing a comprehensive view of the training system. • Reproducible methodology and international comparison: The proposed methodology can be replicated in other contexts, facilitating international comparison and the adoption of best practices. • Extension of knowledge and new perspective: By integrating a participatory approach and developing indicators adapted to local needs, the study extends existing research and offers new perspectives on vocational training evaluation.

The Application of Pearson Correlational Analysis Method in Air Quality Analysis of Beijing-Tianjin-Hebei Region

Based on the analysis of monitoring data on six pollution indexes of SO2, NO2, CO, O3, PM10 and PM2.5 from 53 monitoring points in 7 cities, including Beijing, Tianjin, Shijiazhuang, etc., from April 8 of 2014 to July 23 of 2014, this article adopted Pearson correlation coefficient method to determine the relevance among each pollutant of these cities with the help of SPSS. The results showed that such three leading indexes as SO2, PM10 and PM2.5 had strong correlation in Beijing, Tianjin and main cities of Hebei. Finally, some suggestions and preventive measures for the cooperative governance of air pollution in Beijing-Tianjin-Hebei Region were put forward, hoping this can help them.

Exotic and invasive species compromise the seed bank and seed rain dynamics in forests undergoing restoration at urban regions

The control of exotic and invasive species in areas undergoing recovery is a challenge for ecological restoration and this problem may be even greater in urbanized areas. This study evaluated the seed bank and seed rain of a 5-year-old riparian forest located at the metropolitan region of Belo Horizonte in southeast Brazil, and verified changes over time in the abundance and species richness and whether planted species already were providing propagules to the site. At the forest, fifteen parcels of 100 m2 were distributed randomly and soil samples were collected thrice (October 2011, March 2012 and July 2012) to evaluate the seed bank. Seed traps were installed in nine parcels to collect propagules over 1 year. Propagules were segregated and germinate at greenhouse. Species richness and abundance were estimated, and variance and similarity were quantified. In total, 86 species were recorded in the seed banks, 41.9% natives and 33.7% exotics, with the predominance of herbs (87.2%) and only 7% of trees. Species classified as weeds amounted to 52.3%. In the seed rain 642 seedlings germinated but they belonged to only 10 species, half of them were exotics. Regarding life forms, half of the species were arboreal and dominated the samples, highlighting the exotic species Melia azedarach. The species richness of the seed bank did not differ by season, unlike the seed rain which varied significantly by month. Seed bank and seed rain species had a very low floristic similarity with the list of planted species, suggesting that few planted trees had dispersed or were stocking propagules. Many non-planted species were recorded, mostly exotics and/or invasive that were probably derived from the surrounding urban matrix. These might interrupt the successional dynamics of the restoration forest and compromise the restoration process in the medium and long-terms. Our results highlight: (1) the importance of monitoring areas undergoing restoration; (2) that restoration methods and management actions directed to control exotic and invasive species are particularly important for sites in urban regions; and (3) the relevance of conserving forest fragments as sources of propagules for nearby areas.

Approach to Anomaly Detection in Microservice System with Multi-Source Data Streams

Microservices have become popular in enterprises because of their excellent scalability and timely update capabilities.However,while fine-grained modularity and service-orientation decrease the complexity of system development,the complexity of system operation and maintenance has been greatly increased,on the contrary.Multiple types of system failures occur frequently,and it is hard to detect and diagnose failures in time.Furthermore,microservices are updated frequently.Existing anomaly detection models depend on offline training and cannot adapt to the frequent updates of microservices.This paper proposes an anomaly detection approach for microservice systems with multi-source data streams.This approach realizes online model construction and online anomaly detection,and is capable of self-updating and self-adapting.Experimental results show that this approach can correctly identify 78.85%of faults of different types.