The influence of environmental aesthetics on economic value of housing: an empirical research on virtual environments

Previous studies on house prices tend to focus on parameters such as location, size and view. Such studies ignored the influence of environmental aesthetics on house prices. Considering the scientific evidence that showed a significant effect of environmental aesthetic on people’s environmental preference and behaviour, this study aims to investigate the influence of environmental aesthetic on estimated house prices. Twelve computer-simulated residential environments were created. Computer-simulated images vary on three categories to manipulate complexity and coherence along a street: (1) building location (attached, detached and both), (2) building height (same and different) and (3) building setback (same and different). Forty-seven university students viewed the computer-simulated environments and answered questions to evaluate aesthetic and economic values of each street. For the aesthetic value, participants evaluated the extent to which the environment is arousing, pleasant, exciting, relaxing, complex, coherent and safe. For the economic value, participants estimated market price (for sale and for rent) of a specific apartment on a computer simulation of a residential street. Results showed that estimated sale price was influenced by excitement, coherence and pleasantness; estimated rental price was influenced by complexity, arousal, coherence and pleasantness. This study aimed to pave the way for such studies by introducing an inspiring methodology and highlighting the importance of environmental aesthetics on house prices. Moreover, the findings have applied value for planners, urban designers and real estate agents.     

Optimisation of cutting parameters for minimizing carbon emission and maximising cutting quality in turning process

Modern manufacturing systems are faced with the challenge of reducing the carbon emission related to manufacturing technologies. Machining centres consume large amounts of energy and as a consequence; carbon emissions are generated owing to this consumption. This paper presents a design of experiment work related to the optimisation of machining factors in the turning process of aluminium alloys. Carbon emission and surface quality were concurrently optimised. A set of experimental scenarios was set using a Box–Behnken design and the response surface methodology was applied to get the regression model for the carbon emission and surface roughness during turning process. The relationship between factors and the responses (carbon emission and surface quality) was investigated using surface plots. Furthermore, the desirability function method using the Response Optimizer tool in MINITAB and goal programming methodology was used to obtain the values of the parameters that achieved minimum surface roughness and a minimum quantity of carbon emission.     

Artificial intelligence control for trust-based detection of attackers in 5G social networks

This study introduces a comprehensive framework designed for detecting and mitigating fake and potentially threatening user communities within 5G social networks. Leveraging geo-location data, community trust dynamics, and AI-driven community detection algorithms, this framework aims to pinpoint users posing potential harm. Including an artificial control model facilitates the selection of suitable community detection algorithms, coupled with a trust-based strategy to effectively identify and filter potential attackers. A distinctive feature of this framework lies in its ability to consider attributes that prove challenging for malicious users to emulate, such as the established trust within the community, geographical location, and adaptability to diverse attack scenarios. To validate its efficacy, we illustrate the framework using synthetic social network data, demonstrating its ability to distinguish potential malicious users from trustworthy ones.     

Biohydrogen production by Rhodobacter capsulatus Hup mutant in pilot solar tubular photobioreactor

In this study, a pilot solar tubular photobioreactor was successfully implemented for fed batch operation in outdoor conditions for photofermentative hydrogen production with Rhodobacter capsulatus (Hup⁻) mutant. The bacteria had a rapid growth with a specific growth rate of 0.052 h⁻¹ in the batch exponential phase and cell dry weight remained in the range of 1–1.5 g/L throughout the fed batch operation. The feeding strategy was to keep acetic acid concentration in the photobioreactor at the range of 20 mM by adjusting feed acetate concentration. The maximum molar productivity obtained was 0.40 mol H₂/(m³ h) and the yield obtained was 0.35 mol H₂ per mole of acetic acid fed. Evolved gas contained 95–99% hydrogen and the rest was carbon dioxide by volume.