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.

In agriculture, crop yield and quality are critical for global food supply and human survival. Challenges such as plant leaf diseases necessitate a fast, automatic, economical, and accurate method. This paper utilizes deep learning, transfer learning, and specific feature learning modules (CBAM, Inception-ResNet) for their outstanding performance in image processing and classification. The ResNet model, pretrained on ImageNet, serves as the cornerstone, with introduced feature learning modules in our IRCResNet model. Experimental results show our model achieves an average prediction accuracy of 96.8574% on public datasets, thoroughly validating our approach and significantly enhancing plant leaf disease identification.

An experiment was conducted to assess the effect of psychoenergetic energy in litchi as positive and negative thoughts using a simple meditation technique at ICAR-NRC on Litchi, Muzaffarpur. The plant produced 24.75 g of fruit given positive energy, while the plant with negative thought energy produced 22.12 g of fruit. The fruit and seed weight increased by 11.88% and 13.63%, respectively, due to positive energy. The number of fruit retentions increased by 23.77% due to positive energy. Anthocyanin content in pericarp was increased by 5.45% in plants given positive energy. Fruit qualities were also significantly affected by psychoenergy. TSS (Brix) was significantly increased by 13.54% in plants given positive energy as compared to negative energy, and titratable acidity was reduced by 25% due to positive energy. Ascorbic acid was also increased by 30% in plant given positive thoughts. Sun burn was reduced by 54.76% and fruit cracking by 63.64% due to energy of thought. Fruit borer infestation was reduced by 70%, and mite infestation was reduced by 90% in plants given positive energy. The psychoenergetic potential is vast, and its ability to improve crop yield and quality cannot be overstated. The hidden power of thought is being practiced by all, but mostly people do not know this power and use it in an improper manner. This is a high time when we need to practice generating powerful thoughts to change present-day agriculture and its dependents.

In the rapidly evolving landscape of contemporary business, the strategic alignment of employees with their designated roles is a pivotal determinant of organizational success. Employee misfit, characterized by a misalignment between employees’ skills, interests, and assigned roles, poses formidable challenges to individual and collective performance. This comprehensive research report delves into the intricate implications of employee misfit, explores evolving trends in career consciousness among job seekers, outlines the multifaceted challenges HR managers face, and fervently advocates for implementing a comprehensive selection process to address this prevalent issue effectively. The report underscores the proactive role of management in cultivating a supportive work environment, fostering diverse career pathways, and embedding an inclusive selection framework to confront and mitigate the persistent issue of employee misfit.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.