Accurate Identification of Agricultural Inputs Based on Sensor Monitoring Platform and SSDA-HELM-SOFTMAX Model

The unreliability of traceability information on agricultural inputs has become one of the main factors hindering the development of traceability systems. At present, the major detection techniques of agricultural inputs were residue chemical detection at the postproduction stage. In this paper, a new detection method based on sensors and artificial intelligence algorithm was proposed in the detection of the commonly agricultural inputs in Agastache rugosa cultivation. An agricultural input monitoring platform including software system and hardware circuit was designed and built. A model called stacked sparse denoising autoencoder-hierarchical extreme learning machine-softmax (SSDA-HELM-SOFTMAX) was put forward to achieve accurate and real-time prediction of agricultural input varieties. The experiments showed that the combination of sensors and discriminant model could accurately classify different agricultural inputs. The accuracy of SSDA-HELM-SOFTMAX reached 97.08%, which was 4.08%, 1.78%, and 1.58% higher than a traditional BP neural network, DBN-SOFTMAX, and SAE-SOFTMAX models, respectively. Therefore, the method proposed in this paper was proved to be effective, accurate, and feasible and will provide a new online detection way of agricultural inputs.

MENAKAR WORK FROM EVERYWHERE DI ERA NEW NORMAL

This study aims to add literature by studying the implementation of work from everywhere in government offices and their impact on civil servants. The study was conducted with interviews with resource persons who run work from everywhere (data collection), data reduction (data reduction), presentation of data (display data), and drawing/presentation of conclusions (conclusion drawing/verification). The results of the study showed that First, the function of the organization continues to run well. Second, civil servants have no difficulty carrying out work from everywhere and can fully work at home or elsewhere. Third, the importance of integrity and professionalism supervision on the implementation of work from everywhere where the impact caused by lack of integrity and professionalism on public servants is harmful to the organization. Surveillance can be input monitoring and behavioral monitoring. Fourth, civil servants do not fully agree that work from everywhere is done as a new normal after the pandemic ends. Public servants want to work from the office rather than work from everywhere. Fifth, leadership does not experience significant obstacles in the implementation of work from everywhere.

Data challenges for future plant gene editing: expert opinion

Agricultural data in its multiple forms are ubiquitous. With progress in crop and input monitoring systems and price reductions over the past decade, data are now being captured at an unprecedented rate. Once compiled, organized and analyzed, these data are capable of providing valuable insights into much of the agri-food supply chain. While much of the focus is on precision farming, agricultural data applications coupled with gene editing tools hold the potential to enhance crop performance and global food security. Yet, digitization of agriculture is a double-edged sword as it comes with inherent security and privacy quandaries. Infrastructure, policies, and practices to better harness the value of data are still lacking. This article reports expert opinions about the potential challenges regarding the use of data relevant to the development and approval of new crop traits as well as mechanisms employed to manage and protect data. While data could be of great value, issues of intellectual property and accessibility surround many of its forms. The key finding of this research is that surveyed experts optimistically report that by 2030, the synergy of computing power and genome editing could have profound effects on the global agri-food system, but that the European Union may not participate fully in this transformation.

Comparative analysis of input monitoring conditions for improving the educational performance of urban and rural schools

Оne of the means of our time for the world and domestic education systems is the problem of equalizing the chances of students, for equal access to quality education. State program of the Russian Federation «Development of education» created a set of measures in the regions. In the Yaroslavl region, since 2020, this system of measures has been implemented within the framework of the regional project «Improving the quality of education in secondary schools with low learning outcomes and in schools operating in unfavorable social conditions». Within the framework of this project, comprehensive support was organized for 68 educational organizations from 12 municipal districts of the Yaroslavl region, of which, according to the clustering adopted in the Yaroslavl region, 20 general education organizations belong to the cluster of urban schools. The authors of the article reveal a number of main elements of the regional program, outline the theoretical prerequisites for dividing schools in the Yaroslavl region into two categories: schools with low learning outcomes and schools operating in unfavorable social conditions, and present the results of a comparative analysis of data from input monitoring of indicators of urban and rural schools participating in the regional project. The article describes the procedure for analyzing personnel support and human resources for optimizing the educational process in schools, aimed at improving educational results. A statistical portrait of students in urban and rural schools has been compiled in relation to their participation in student self-government bodies, passing the certification procedure, and completing additional education programs.