Facial emotion recognition is a crucial process for many applications and is still unresolved. Historically, emotional recognition has usually been achieved through artificial intelligence techniques such as Convolutional Neural Networks. However, this approach is quite expensive in terms of computational power and complexity. To alleviate this problem, we propose a lightweight CNN for facial emotion recognition, called Custom Lightweight CNN-based Model (CLCM), based on the well-known MobileNetV2 architecture. Performance was evaluated on four public datasets, FER-2013, RAF-DB, AffectNet, and CK+, where seven facial emotions were detected. The CLCM model was compared to the well-known MobileNetV2 and ShuffleNetV2 architectures. The CLCM performance results were close to or better than the more complex models. Specifically, in the FER-2013 dataset, CLCM achieved an accuracy of 63%, while MobileNetV2 was 58%, and ShuffleNetV2 65%. In the RAF-DB dataset, CLCM showed 84%, MobileNetV2 73%, and ShuffleNetV2 80%. Lastly, in the AffectNet dataset, MobileNetV2 and ShuffleNetV2 showed an accuracy of 57%, while CLCM showed 54%. The results obtained from this study establish CLCM as one of the efficient models in FER. Although CLCM is a smaller model in terms of parameters (2.3 Million) compared to MobileNetV2 (3.5 Million) and ShuffleNetV2 (3.9 Million), it showed good results in almost all analyses. The reduced CLCM’s computational power allows its application in enhanced human-computer interaction, affective computing, and personalized user experience, especially for real-world scenarios such as psychological and medical assessment, automotive (real-time driver emotional state), and vulnerable individual care where limited resource systems are commonly employed and real-time and reliable response are strongly recommended.

Does social media addiction impair the well-being of non-clinical individuals? Despite the Internet being able to be considered as a promoting factor for individual empowerment, previous literature suggests that the current massive availability of Information and Communication Technologies (ICT) may be dangerous for users’ well-being. This article discusses the relationship between the most used social media addiction measures (i.e., the Bergen Facebook Addiction Scale—BFAS, the Bergen Social Media Addiction Scale—BSMAS) and well-being. A systematic review considering all the publications indexed by PsycInfo, PsycArticles, PubMed, Science Direct, Sociological Abstracts, Academic Search Complete, and Google Scholar databases was performed to collect the data. Ten of 635 studies were included in the qualitative synthesis. Overall, most of the included works captured a negative but small relationship between BFAS/BSMAS and well-being, across multiple definitions and measurement.

Phubbing could be defined as a new form of addiction; however, checking the phone and ignoring the speaker could also be linked to the increased availability of virtual social environments. We developed a multidimensional model for phubbing considering psychological dimensions and information and communication technology related habits. We collected data through online questionnaires and surveys. The best model obtained from our data was constituted by Information and Communication Technologies’ (ICTs) usage behaviours, Trait Anxiety, Virtual Sense of Community and Neuroticism. Finally, our study confirmed a strong connection between phubbing and online addiction behaviours.

There is lack of evidence about policies and programmes to eradicate workplace incivility, lateral violence and bullying among nurses. Prevention of these matters should start from spreading information inside continue educational settings and university nursing courses.

The prolonged lockdown imposed to contain the COrona VIrus Disease 19 COVID-19 pandemic prevented many people from direct contact with nature and greenspaces, raising alarms for a possible worsening of mental health. This study investigated the effectiveness of a simple and affordable remedy for improving psychological well-being, based on audio-visual stimuli brought by a short computer video showing forest environments, with an urban video as a control. Randomly selected participants were assigned the forest or urban video, to look at and listen to early in the morning, and questionnaires to fill out. In particular, the State-Trait Anxiety Inventory (STAI) Form Y collected in baseline condition and at the end of the study and the Part II of the Sheehan Patient Rated Anxiety Scale (SPRAS) collected every day immediately before and after watching the video. The virtual exposure to forest environments showed effective to reduce perceived anxiety levels in people forced by lockdown in limited spaces and environmental deprivation. Although significant, the effects were observed only in the short term, highlighting the limitation of the virtual experiences. The reported effects might also represent a benchmark to disentangle the determinants of health effects due to real forest experiences, for example, the inhalation of biogenic volatile organic compounds (BVOC).