Recommendations included factoring in personal and work stresses, promoting the use of effective coping strategies and maintaining supportive social relationships.

Stress is ubiquitous in the nursing profession and is also prevalent in Asian countries, particularly the "four tigers of Asia": Singapore, Hong Kong, Taiwan, and South Korea. Based on the theoretical framework of Lazarus and Folkman (1984), the present review of the nursing literature aims to identify sources and effects of stress in Singaporean nurses and the coping strategies they use. Nurses reported major stressors including shortage of staff, high work demands and conflict at work. Common coping strategies included problem orientation, social support and relaxation techniques. Several studies reported nurses' intent to leave the profession. Recommendations to minimize the impact of stress include in-service programs to facilitate a problem-solving approach to resolving work-related issues such as conflict. Relaxation therapy and debriefing sessions may also help in reducing negative effects of work stressors. Finally, nurses' emotional coping can be enhanced by strengthening sources of social support, particularly from family.

In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques. Index Terms-Maximum power point tracking (MPPT), artificial intelligence (AI), fuzzy logic control (FLC), artificial neural network (ANN), genetic algorithm (GA), swarm intelligence (SI), machine learning (ML).

This study paper presents a comprehensive review of virtual inertia (VI)-based inverters in modern power systems. The transition from the synchronous generator (SG)-based conventional power generation to converter-based renewable energy sources (RES) deteriorates the frequency stability of the power system due to the intermittency of wind and photovoltaic (PV) generation. Unlike conventional power generation, the lack of rotational inertia becomes the main challenge to interface RES with the electrical grid via power electronic converters. In the past several years, researchers have addressed this issue by emulating the behavior of SG mathematically via pulse width modulation (PWM) controller linked to conventional inverter systems. These systems are technically known as VI-based inverters, which consist of virtual synchronous machine (VSM), virtual synchronous generator (VSG), and synchronverter. This paper provides an extensive insight into the latest development, application, challenges, and prospect of VI application, which is crucial for the transition to low-carbon power system.