With rapid population growth, urban designers face tremendous challenges to accommodate the increasing size of the population in urban areas while simultaneously considering future environmental, social, and economic impacts. A “smart city” is an urban development vision that integrates multiple information and communication technologies to manage the assets of a city, including its information systems, transportation systems, power plants, water supply networks, waste management systems, and other community services provided by a local department. The goal of creating a smart city is to improve the quality of life of citizens by using technology and by addressing the environmental, social, cultural, and physical needs of a society. Data modeling and data visualization are integral parts of planning a smart city, and planning professionals currently seek new methods for real-time simulations. The impact analysis of “what-if scenarios” frequently takes a significant amount of time and resources, and virtual reality (VR) is a potential tool for addressing these challenges. VR is a computer technology that replicates an environment, whether real or imagined, and simulates the physical presence and environment of a user to allow for user interaction. This paper presents a review of the capacity of VR to address current challenges in creating, modeling, and visualizing smart cities through material modeling and light simulation in a VR environment. This study can assist urban planners, stakeholders, and communities to further understand the roles of planning policies in creating a smart city, particularly in the early design stages. The significant roles of technologies, such as VR, in targeting real-time simulations and visualization requirements for smart cities are emphasized.

The commercial release of affordable, low-cost, and consumer-ready virtual reality (VR) devices has increased the accessibility for researchers to investigate the benefits of VR technology including those aimed at education and training. VR technology provides several opportunities that may provide benefits over traditional training methods, this is particularly relevant for safety training due to its ability to safely simulate dangerous scenarios that would otherwise be difficult to access. When implementing a new technology, it is important to evaluate and validate its effectiveness. This paper presents a systematic review of VR safety-relevant training studies that perform an evaluation of their effectiveness. This comprehensive review includes 136 studies published between 2016 and August 2021. Results presented in this paper include application domains, study objectives, study designs, and evaluation measures. Results show that the majority of studies were applicable to health services with the majority focusing on effectiveness evaluation using true- or quasi-experimental design. This study then categorizes each reported evaluation measure into one of the four levels in Kirkpatrick’s model for training evaluation, results showed that the majority of studies evaluated learning (72.06%) and reaction (66.18%) levels with very few studies evaluating behavior and results levels. This study concludes by providing insights and recommendations to help future researchers make informed decisions when designing an effectiveness evaluation study for VR safety-relevant training applications.

Understanding built environment exposure as a component of environmental enrichment has significant implications for mental health, but little is known about the effects design characteristics have on our emotions and associated neurophysiology. Using a Cave Automatic Virtual Environment while monitoring indoor environmental quality (IEQ), 18 participants were exposed to a resting state (black), and two room scenes, control (white) and condition (blue), to understand if the color of the virtual walls affected self‐report, autonomic nervous system, and central nervous system correlates of emotion. Our findings showed that exposure to the chromatic color condition (blue) compared to the achromatic control (white) and resting‐state (black, no built environment) significantly increased the range in respiration and skin conductance response. We also detected a significant increase in alpha frontal midline power and frontal hemispheric lateralization relative to blue condition, and increased power spectral density across all electrodes in the blue condition for theta, alpha, and beta bandwidths. The ability for built environment design to modulate emotional response has the potential to deliver significant public health, economic, and social benefits to the entire community. The findings show that blue coloring of the built environment increases autonomic range and is associated with modulations of brain activity linked to emotional processing.

The rapidly increasing use of renewable energy resources in power generation systems in recent years has accentuated the need to find an optimum and efficient scheme for forecasting meteorological parameters, such as solar radiation, temperature, wind speed, and sun exposure. Integrating wind power prediction systems into electrical grids has witnessed a powerful economic impact, along with the supply and demand balance of the power generation scheme. Academic interest in formulating accurate forecasting models of the energy yields of solar energy systems has significantly increased around the world. This significant rise has contributed to the increase in the share of solar power, which is evident from the power grids set up in Germany (5 GW) and Bavaria. The Spanish government has also taken initiative measures to develop the use of renewable energy, by providing incentives for the accurate day-ahead forecasting. Forecasting solar power outputs aids the critical components of the energy market, such as the management, scheduling, and decision making related to the distribution of the generated power. In the current study, a mathematical forecasting model, optimized using differential evolution and the particle swarm optimization (DEPSO) technique utilized for the short-term photovoltaic (PV) power output forecasting of the PV system located at Deakin University (Victoria, Australia), is proposed. A hybrid self-energized datalogging system is utilized in this setup to monitor the PV data along with the local environmental parameters used in the proposed forecasting model. A comparison study is carried out evaluating the standard particle swarm optimization (PSO) and differential evolution (DE), with the proposed DEPSO under three different time horizons (1-h, 2-h, and 4-h). Results of the 1-h time horizon shows that the root mean square error (RMSE), mean relative error (MRE), mean absolute error (MAE), mean bias error (MBE), weekly mean error (WME), and variance of the prediction errors (VAR) of the DEPSO based forecasting is 4.4%, 3.1%, 0.03, −1.63, 0.16, and 0.01, respectively. Results demonstrate that the proposed DEPSO approach is more efficient and accurate compared with the PSO and DE.

The widespread use and inherent risks associated with low-voltage electrical equipment require all workers to understand its hazards and how to manage them. Despite being the most commonly used method for raising safety awareness, lecture-based training often proves ineffective. Virtual reality (VR) allows the user to be immersed in a virtual environment and actively participate in practical training while maintaining their safety, which can potentially result in engaging and effective training. This paper investigates the effectiveness of using immersive VR for low-voltage (LV) electrical safety training to understand the potential benefits of VR technology for industrial safety training applications. A within-group experimental design was employed with 18 participants undertaking the LV VR training. The effectiveness of the training was evaluated by measuring participants' reactions, learning, and training duration. Participants' learning was measured before, immediately after, and four weeks after the training, whereas reaction and suitability of training duration were measured after the training. Participants reported highly positive reactions to the LV VR training, particularly regarding their level of engagement, intention to use the system in the future, and enjoyment. Knowledge test scores significantly improved immediately after training with high effect sizes. Although scores decreased significantly four weeks after training, they remained significantly higher compared to before training. The training duration, ranging from 29 to 44 minutes was considered suitable for the training. LV VR training effectively elicited positive reactions from trainees and supporting trainees to acquire and retain safety information while maintaining appropriate training duration. VR emerges as a viable alternative training method worth exploring for organizations seeking to improve their safety training programs. VR combines educational and entertainment values, facilitating enjoyable learning experiences.