With an enormous growth in the number of users for social media, it becomes important to understand its impact on the society. This paper is a small step in this direction by analyzing the impact of social media on the emotional health of its users. The paper in particular focuses on the impact of WhatsApp usage. In the study, authors have focused their research on 225 users. In this survey authors found that WhatsApp has a significant impact on the humans these days. It adversely impacts the youth and their education, behavior and routine life. This app is found to be highly addictive, which leaves a trace that becomes difficult to control. The impact of this application is so engrossing that users give up their real world interest and whole emotional quotient is restricted to the app. Their happiness or sadness depends on the reply that they receive from other users. They cannot control themselves from constantly chatting, replying and sharing of ideas. Hence, it is found during the study that some findings are alarming and needs to be controlled. It is noticed during the survey that WhatsApp has greatly influenced the life style of its users and therefore its usage should be monitored and controlled to avoid any adverse effect on emotional health of its users. 

This case study aimed to determine the effects of adult learning on employee development and performance.  Specifically, the authors sought to discern whether the provision of an annual education allotment impacted employees’ learning; whether the availability of a no-cost online education and training tool promoted voluntary learning and professional development; and, whether informal but mandatory workplace trainings promoted active performance-based learning and self-development.  Data from a United States (US) Government office were collected and analyzed using mixed methods including quantitative and qualitative analyses.  Although three different learning modalities were available, the realism was that in all learning activities, intrinsic motivation was an unavoidable paradigm that evokes and sustains effective learning, and as a result, positive job performance.  In other words, a reciprocal relationship between motivation and achievement must be present to achieve and ensure continued success. 

On the Wechat platform, the current article push is mainly completed by the Wechat Public Account, but it is not perfect in the aspects of user information collection, user service, data storage and management. With economic development and progress of the times, people seek development in spiritual and cultural aspects. This program "One Thing One Story" uses Wechat Web Developer Tools as the medium and Wechat Small Program and Cloud Development as the platform. The purpose of push management platform is "use at any time". Small program cloud development has a relatively complete cloud background. It does not need to rebuild the server in the development cycle. Through the relevant interface, small program development can be started and time cost can be reduced. Using JavaScript, CSS style, JSON database and other technologies, we can realize user data collection, article push, push classification management, push data storage, user praise collection and other functions. This program is applied to article pushing, cultural dissemination and other aspects. Through the platform of Wechat applet, the dream of "accessible" can be realized. 

Collaborative filtering recommender system suffers from data sparsity problem due to its reliance on numerical ratings to provide recommendations to users. This problem makes it difficult for the system to compute accurate similar neighbours for the items and provide good quality recommendations. Existing methods fail to pre-process the missing ratings of the new items and to predict cold items to the active users which lead to poor quality recommendations. In this work, a sparsity reduction method is presented to improve the quality of recommendations. The method utilises Bi-Separated clustering algorithm to cluster the ratings matrix simultaneously into users and items bi-clusters based on ratings classification. It also employs Bi-Mean Imputation algorithm to fill the missing ratings in the bi-clusters using the estimated means. The method then performs the traditional collaborative filtering process on the new rating matrix for cold items prediction. The experimental results demonstrated that compared to the existing method, the proposed BiSCBiMI improves density of the rating matrix by 5.75%, 10.73% and 7.35% as well as Mean Absolute Error (MAE)  of the new items prediction for all of the considered datasets.  The results indicated that, the proposed approaches are effective in reducing the data sparsity problem as well as items prediction, which in turn returns good quality recommendations.

Image noise denoising is a very important task in image processing. Aiming at the shortcomings of traditional median filtering to handle image impulse noise, an approach based on Salp Swarm Algorithm (SSA) to eliminate image impulse noise is presented in the paper. In this method, the improved extremum method is used to detect the position of impulse noise pixels, and then the Salp Swarm algorithm is used to find the optimal pixel value instead of the noise pixel to complete the denoising process of the image. Experimental results testfies that image impulse noise could be effectively filtered out through the proposed method and the manipulated image is clear and more detail could be revealed for human vision.